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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]

This study examines the climatology and trends in climate in Guatemala, Honduras, and El Salvador over the past four decades, against the background of potential impacts on rainfed agriculture, livelihoods, and migration. The results show that there has been a significant warming of surface temperatures, an earlier start to the monsoon, a drier and longer mid‐summer drought, and a delay in the second peak of precipitation from September to October. These changes have led to an increase in vapour pressure deficit (VPD) in northern Guatemala and along the Pacific coast in winter. High VPD can stress plants and lead to reduced yields. The study also finds that the thresholds that cause a decline in coffee yield have already been reached in El Salvador, but the average VPD has also risen in Guatemala and Honduras over the past 42 years. Maize yields have also been negatively affected with an inverse relationship with daily maximum temperatures during the summer flowering season. Observed changes and trends in these climate factors are believed to have direct implications for crop yields and livelihoods, potentially driving shifts in migration patterns. [ABSTRACT FROM AUTHOR]

The rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long‐term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans‐Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long‐term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra‐annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic. [ABSTRACT FROM AUTHOR]

Three‐hourly data from two satellite rainfall estimates products, PERSIANN and TMPA, are analysed to document the seasonal patterns of diurnal rainfall distribution over the Congo Basin and neighbouring areas. PERSIANN data for 2001–2017, at a one‐hour time‐scale, are further used to identify rain cells (≥4 mm·h−1) in an attempt to explain the diurnal rainfall variations. Over land areas, an afternoon rainfall maximum is clearly shown, but over much of the region only a minor part of the rains (20%–30%) falls in the wettest 3‐h period. Substantial rains (often 50%–60%) occur in the evening and at night, as a progressively delayed peak from east to west, but a seasonal change is found in the meridional propagation of the peak diurnal rainfall, in a south‐westerly direction in January, and a north‐westerly direction in July. Rain cells have prominent genesis areas west of high terrain, but can develop over most regions, with a peak genesis time slightly ahead the diurnal phase of the rains. The size, mean lifetime and mean rainfall intensity of the rain cells are strongly related to each other and display a semi‐annual cycle not fully in phase with the seasonal cycle of the rains. The mean rain cell propagation speed (6.7 m·s−1) is much lower than in previous studies, which focused on mesoscale convective systems. Rain cells which have a longer lifetime move much faster, the mean speed of those lasting less than 6 h being half that of those lasting at least 24 h. Most (86%) of the mobile rain cells propagate westward, but the meridional component of their propagation shows an annual cycle (southward in austral summer, northward in boreal summer) which matches the mid‐tropospheric winds and explains the seasonal changes in the diurnal rainfall peak. [ABSTRACT FROM AUTHOR]

Concerns about perceived widespread declines in insect numbers have led to recognition of a requirement for long-term monitoring of insect biodiversity. Here we examine whether an existing, radar-based, insect monitoring system developed for research on insect migration could be adapted to this role. The radar detects individual larger (greater than 10 mg) insects flying at heights of 150–2550 m and estimates their size and mass. It operates automatically and almost continuously through both day and night. Accumulation of data over a 'half-month' (approx. 15 days) averages out weather effects and broadens the source area of the wind-borne observation sample. Insect counts are scaled or interpolated to compensate for missed observations; adjustment for variation of detectability with range and insect size is also possible. Size distributions for individual days and nights exhibit distinct peaks, representing different insect types, and Simpson and Shannon–Wiener indices of biodiversity are calculated from these. Half-month count, biomass and index statistics exhibit variations associated with the annual cycle and year to year changes that can be attributed to drought and periods of high rainfall. While species-based biodiversity measures cannot be provided, the radar's capacity to estimate insect biomass over a wide area indicates utility for tracking insect population sizes. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

Based on the disaster-pregnant environment and development characteristics of landslide disasters in the western region of Henan Province, a generalized model was established by taking the "oblique-cut" locking rock slope in the layered rock slope as the research object. The numerical simulation method was used to analyze the deformation and failure mechanism and stability influence law of the oblique-cut locking rock slope in western Henan under rainfall conditions. The results show that the inclination angle of the weak interlayer directly affects the deformation and failure characteristics of the slope rock mass. With the increase of the geometric parameters of the slope and the inclination angle of the weak interlayer, the failure mechanism is manifested as the slip shear failure along the level at the foot of the slope → the slip shear failure along the level at the foot of the slope (the sliding surface moves upward) → the shear failure in the middle of the slope surface → the slip shear failure along the level at the foot of the slope (the sliding surface moves downward) → the uplift shear failure at the lower edge of the rock layer. When the dip angle of the weak interlayer is constant, the slope stability decreases gradually with the increase in slope gradient and slope height, and the geometric factors of the slope control the overall change trend of the slope stability coefficient. When the slope is greater than 55° and the slope height is greater than 55 m, the shear stress of the slope locking section exceeds its shear strength, and the probability of landslide instability is greatly increased. [ABSTRACT FROM AUTHOR]

The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000 MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China's mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206 ; Chen, 2024). [ABSTRACT FROM AUTHOR]

Background: Sucrose accumulation in sugarcane is affected by several environmental and genetic factors, with plant moisture being of critical importance for its role in the synthesis and transport of sugars within the cane stalks, affecting the sucrose concentration. In general, rainfall and high soil humidity during the ripening stage promote plant growth, increasing the fresh weight and decreasing the sucrose yield in the humid region of Colombia. Therefore, this study aimed to identify markers associated with sucrose accumulation or production in the humid environment of Colombia through a genome-wide association study (GWAS). Results: Sucrose concentration measurements were taken in 220 genotypes from the Cenicaña's diverse panel at 10 (early maturity) and 13 (normal maturity) months after planting. For early maturity data was collected during plant cane and first ratoon, while at normal maturity it was during plant cane, first, and second ratoon. A total of 137,890 SNPs were selected after sequencing the 220 genotypes through GBS, RADSeq, and whole-genome sequencing. After GWAS analysis, a total of 77 markers were significantly associated with sucrose concentration at both ages, but only 39 were close to candidate genes previously reported for sucrose accumulation and/or production. Among the candidate genes, 18 were highlighted because they were involved in sucrose hydrolysis (SUS6, CIN3, CINV1, CINV2), sugar transport (i.e., MST1, MST2, PLT5, SUT4, ERD6 like), phosphorylation processes (TPS genes), glycolysis (PFP-ALPHA, HXK3, PHI1), and transcription factors (ERF12, ERF112). Similarly, 64 genes were associated with glycosyltransferases, glycosidases, and hormones. Conclusions: These results provide new insights into the molecular mechanisms involved in sucrose accumulation in sugarcane and contribute with important genomic resources for future research in the humid environments of Colombia. Similarly, the markers identified will be validated for their potential application within Cenicaña's breeding program to assist the development of breeding populations. [ABSTRACT FROM AUTHOR]

Floods affect communities and ecosystems worldwide, emphasizing the importance of identifying their precursors and enhancing resilience to these events. Here, we calculated Antecedent Total Water Storage (ATWS) anomalies from the new 5-day (5D) Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) satellite solutions to enhance the detection of pre-flood and active flood conditions and to map post-flood storage anomalies. The GRACE data were compared with ~ 3300 flood events reported by the Dartmouth Flood Observatory (2002–2021), revealing distinct ATWS precursor signals in 5D solutions, in contrast to the monthly solutions. Specifically, floods caused by saturation-excess runoff—triggered by persistent rainfall, monsoonal patterns, snowmelt, or rain-on-snow events—show detectable ATWS increases 15 to 50 days before and during floods, providing a valuable opportunity to improve flood monitoring. These 5D solutions also facilitate a more rapid mapping of post-flood storage changes to assess flood recovery from tropical cyclones and sub-monthly weather extremes. Our findings show the promising potential of 5D GRACE solutions, which are still in the development phase, for future integration into operational frameworks to enhance flood detection and recovery, facilitating the rapid analysis of storage changes relative to monthly solutions. [ABSTRACT FROM AUTHOR]

This work analyses 10 years (from 2009 to 2018) of long‐range Saharan dust advections (SDAs) and their correlation with the precipitation and radiative budget over the Central Mediterranean region. The identification of the SDAs is based on back‐trajectories (BTs) of air mass and complementary measurements of the dust deposited at the site of Monte Martano in Central Italy. The associated synoptic circulation weather types (CWT), precipitation and radiation variables have been estimated by using ERA5 and CAMS reanalysis, satellite data and raingauge observations. It is found that the 50% of all SDAs occur under a CWT characterized by an upper level trough over the Western Mediterranean and a high‐pressure system over the Eastern Mediterranean. Strong southerly winds, large vertical integral of water vapour transport and a positive anomaly of 2 m temperature are associated with dust uplift and transport. The effect of dust on rainfall intensity puts forward the important modulation of the aerosol effects on the radiative budget with a latitudinal dependence. On dusty days, intense rainfall increases over the northern Central Mediterranean and light rainfall is suppressed in the southern Central Mediterranean, pointing out a relevant correlation between dust and the intensification of extreme events. Other than affecting rainfall intensity, the stronger surface heating over the southern Central Mediterranean, which is correlated to a higher dust optical depth, causes a local maximum of sea surface temperature (SST) and near‐surface temperature anomaly. [ABSTRACT FROM AUTHOR]

We study the sensitivity of South Asian Summer Monsoon (SASM) precipitation to Southern Hemisphere (SH) subtropical Absorbed Solar Radiation (ASR) changes using Community Earth System Model 2 simulations. Reducing positive ASR biases over the SH subtropics impacts SASM, and is sensitive to the ocean basin where changes are imposed. Radiation changes over the SH subtropical Indian Ocean (IO) shifts rainfall over the equatorial IO northward causing 1–2 mm/day drying south of equator, changes over the SH subtropical Pacific increases precipitation over northern continental regions by 1–2 mm/day, and changes over the SH subtropical Atlantic have little effect on SASM precipitation. Radiation changes over the subtropical Pacific impacts the SASM through zonal circulation changes, while changes over the IO modify meridional circulation to bring about changes in precipitation over northern IO. Our findings suggest that reducing SH subtropical radiation biases in climate models may also reduce SASM precipitation biases. Plain Language Summary: Precipitation from South Asian Summer Monsoon (SASM) is of high significance to the livelihoods of over a billion people. As the global climate continues to evolve, it is essential to have a clear understanding of the possible future changes to the SASM. However, current state‐of‐the‐art climate models have difficulties in simulating climatological mean SASM precipitations. Here we study sensitivity of SASM precipitation to subtropical southern ocean radiation as one of the possible causes of SASM precipitation bias. Our experiments indicate that SASM precipitation is sensitive to southern hemisphere (SH) subtropical radiation changes particularly to those in subtropical Pacific. These findings imply that improving SH subtropical radiation biases might improve SASM precipitation simulations in climate models. Key Points: We test if biases in southern hemisphere shortwave radiation contributes to biases in South Asian summer monsoon precipitation in the CESM2Reducing incoming shortwave radiation in the subtropical southern hemisphere reduces dry biases over continental South AsiaThis effect is mostly due to forcing in the South Pacific, with less impact from the Atlantic or Indian Ocean [ABSTRACT FROM AUTHOR]

Convective Quasi‐Equilibrium (CQE) is often adopted as a useful closure assumption to summarize the effects of unresolved convection on large‐scale thermodynamics, while existing efforts to observationally validate CQE largely rely on specific spatial domains or sites rather than the source of CQE constraints—deep convection. This study employs a Lagrangian framework to investigate leading temperature perturbation patterns near deep convection, of which the centers are located by use of an ensemble of satellite measurements. Temperature perturbations near deep convection with high peak precipitation are rapidly adjusted toward the CQE structure within the [−2, 1] hours centered on peak precipitation. The top 1% precipitating deep convection constrains neighboring free‐tropospheric leading perturbations up to 9°. Notable CQE validity beyond a 1° radius is observed when peak precipitation exceeds the 93rd percentile. These findings suggest that only a small fraction of deep convection with extreme precipitation shapes tropical free‐tropospheric temperature patterns dominantly. Plain Language Summary: Convective Quasi‐Equilibrium (CQE) is a concept in atmospheric science that explains a state where the influence of deep convection (cumulonimbus clouds) and large‐scale atmospheric forces is balanced, causing certain thermodynamic properties to adjust toward specific reference profiles. Previous studies have focused on how temperature changes relate to the CQE structure but in specific regions or sites while this study aims areas near deep convection—supposedly the source of CQE constraints. Using a unique framework with data from multiple satellites, we track the evolution of temperature patterns near deep convection and find temperatures near deep convection with extreme rainfall are adjusted toward the CQE structure rapidly within 3 hr of maximum rainfall. However, only the deep convection with top 7% extreme rainfall can effectively affect nearby temperature pattern beyond 1°, with the top 1% influencing up to an 9° radius. These findings highlight the dominant impact of a small fraction of deep convection, particularly those with extreme rainfall, on nearby temperature perturbation patterns. Key Points: Tropical temperature perturbations near extreme deep convection rapidly conform to convective quasi equilibrium in a three‐hour windowOnly the top 7% precipitating deep convection can modulate hourly tropical temperature patterns beyond a 1° radiusTop 1% precipitating deep convection constrains nearby temperature perturbations up to an 9° radius during peak precipitation [ABSTRACT FROM AUTHOR]

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]

Anomalous tropical longwave cloud‐radiative heating of the atmosphere is generated when convective precipitation occurs, which plays an important role in the dynamics of tropical disturbances. Defining the observed cloud‐radiative feedback as the reduction of top‐of‐atmosphere longwave radiative cooling per unit precipitation, the feedback magnitudes are sensitive to the observed precipitation data set used when comparing two versions of Global Precipitation Climatology Project, version 1.3 (GPCPv1.3) and the newer version 3.2 (GPCPv3.2). GPCPv3.2 contains larger magnitudes and variance of daily precipitation, which yields a weaker cloud‐radiative feedback in tropical disturbances at all frequencies and zonal wavenumbers. Weaker cloud‐radiative feedbacks occur in GPCPv3.2 at shorter zonal lengths on intraseasonal timescales, which implies a preferential growth at planetary scales for the Madden‐Julian oscillation. Phase relationships between precipitation, radiative heating, and other thermodynamic variables in eastward‐propagating gravity waves also change with the updated GPCPv3.2. Plain Language Summary: High‐altitude, widespread anvil clouds are generated when heavy convective precipitation occurs in the tropics. These clouds are not only a passive product produced by convection, but they also can subsequently enhance convection by trapping upward infrared radiative flux emitted by the Earth, effectively heating the atmosphere. This additional radiative heating effect can induce upward motion in the tropics, supporting the convective systems by transporting more humid air from below. However, the strength of this cloud‐radiative feedback is hard to estimate because global, continuous observations of surface precipitation are difficult to derive. In this study, the strength of the radiative feedback is calculated using the same product of observed radiative heating against two different observational precipitation products. A newer improved precipitation product yields much weaker radiative feedback strengths for all types of tropical weather systems. In addition, cloud‐radiative heating is found to substantially lag behind precipitation in certain fast, eastward‐propagating tropical rainfall systems in the newer precipitation product, unlike the older one. Why such a lag exists is unclear. The discrepancy of the estimation of cloud‐radiative feedback strengths and properties in the older versus the newer precipitation products indicates that our understanding of mechanisms supporting tropical disturbances is still incomplete. Key Points: The updated Global Precipitation Climatology Project (GPCP) precipitation product has more frequent high rain rates, yielding a weaker longwave radiative feedbackThe updated radiative feedback supports less moistening of the Madden‐Julian oscillation, but imposes stronger planetary scale selectionThe phase relationship between precipitation and thermodynamic fields in eastward‐propagating tropical waves are sensitive to GPCP versions [ABSTRACT FROM AUTHOR]

[Display omitted] • Erosion-based prioritization using a comprehensive approach is vital. • The Shazand sub-Watersheds, Iran, were prioritized using the game theory. • The Shazand sub-watersheds were prioritized differently in various scenarios. • The game theory-based Condorcet approach holistically and successfully ranked sub-watersheds. The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures. [ABSTRACT FROM AUTHOR]

Impact of rainfall and slopes on soil organic carbon (SOC) dynamics was assessed under tea and coffee plantations, land covers, in Western Ghats Mountain (WGM). Severe water erosion in the western side of WGM by heavy rainfall and land sloppiness recorded less SOC (3.07%) compared to the eastern side (4.03%) in tea plantations. Similar trend in SOC was observed in coffee plantations, with 1.98% in western side and 3.25% in eastern side. Loss of soil organic carbon stock under tea plantations in western sides was 0.346 million tonnes and in coffee plantations it was 0.190 million tonnes. Hence, the total organic carbon loss was 0.536 million tonnes. Highest SOC content and stocks were recorded in steep slopes in tea and coffee plantations due to dense biomass cover and adoption of soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision‐makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the maximum overlap discrete wavelet transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine‐learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to 3 days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from three rainfall observation stations located in Turkey are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash‐Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction. [ABSTRACT FROM AUTHOR]

This study assesses the skill of existing indices and newly introduced sea surface temperature (SST) indices in predicting rainfall over East Africa (EA) during short rains from September to December (SOND) and long rains from March to May (MAM). The existing and newly introduced indices were selected based on grid point correlation and spatial mean correlation. West–east SST gradient over the Indian Ocean (WEIO), north–south SST gradient over the Pacific Ocean (NSPO), anomalies over the western Indian Ocean (WIO) and anomalies over the eastern Pacific Ocean (EPO) from newly developed indices, and bivariate ENSO time series (BEST), Oceanic Niño Index (ONI), Niño3.4, western Pacific gradient (WPG) and dipole index moment (DMI) from existing indices have a positive correlation with SOND rainfall at 5% (p<0.05$$ p<0.05 $$) significance level. West–east SST gradient over the Pacific Ocean (WEPO) and SST anomalies over the southern Pacific Ocean (SPO) from new indices and Southern Oscillation Index (SOI) from existing indices have a significant negative correlation with SOND rainfall over EA. SST anomalies over central and eastern Pacific and Indian oceans significantly correlate with SOND rainfall over southern EA (SEA) and equatorial EA (EEA). Rainfall over northern EA (NEA) has a significant negative correlation with SST over the east and central Pacific oceans. Negative correlation extends northeast and southeast from North of Australia over the Pacific Ocean during MAM. WEIO, WIO, NSPO, Niño3.4, Atlantic Oscillation (AO) and WPG have shown strong and significant (p<0.05$$ p<0.05 $$) relations at least over one subregion. The new SST and existing oceanic indices have a stronger and more significant correlation with short rains than long rains over EA, except in the northern part of EA. Correlation, RMSE and skill score of regression models revealed that new indices showed higher performance than existing indices over EEA and SEA in predicting rainfall, unlike over NEA during both seasons. [ABSTRACT FROM AUTHOR]

This study examines the convergence between traditional and scientific knowledge regarding the use of fire and its potential to trigger wildfires, with possible impacts on ecosystems and human well-being. The research encompasses three distinct natural regions of Ecuador: the coast, the highlands, and the Amazon. Data on traditional fire use were collected through semi-structured interviews with 791 members from five local communities. These data were compared with climatic variables (rainfall (mm), relative humidity (%), wind speed (km/h), and wind direction) to understand the climatic conditions conducive to wildfires and their relationship with human perceptions. Furthermore, the severity of fires over the past 4 years (2019–2022) was assessed using remote sensing methods, employing the Normalized Burn Ratio (NBR) and the difference between pre-fire and post-fire conditions (NBR Pre-fire–NBR Post-fire). The results revealed a significant alignment between traditional knowledge, climatic data, and many fires, which were of low severity, suggesting potential benefits for ecosystems. These findings not only enable the identification of optimal techniques and timing for traditional burns but also contribute to human well-being by maintaining a harmonious balance between communities and their environment. Additionally, they provide valuable insights for the development of more inclusive and effective integrated fire management strategies in these natural areas of Ecuador. [ABSTRACT FROM AUTHOR]

Bangladesh is a highly climate vulnerable region. Among the many areas of Bangladesh, the haor region is considered as one of the most climate change sensitive areas. Again, the nation greatly depends on the agricultural produce of the haor area. So, adoption to climate change strategies is vital for the farming community of Bangladesh. The current study was conducted based on the idea of understanding the depth of knowledge of farmers regarding climate change, identifying the currently practiced climate change adaptation strategies and to know the influential factors behind the adoption of those practices. The study was drawn on primary data collected from 300 farmers of the Dingapota haor of Mohanganj upazila under Netrokona district of Bangladesh through a structured interview schedule. Descriptive analysis, adaptation Strategy Index (ASI), and Pearson's product correlation coefficient (r) were used to analyze data with the help of Excel and SPSS. The study found that the intensity of flash floods, short winter seasons, and unpredictable rainfall have significantly increased. Most respondents believe that certain parts of the climatic conditions are getting worse. Farmers' knowledge about crop diversification secured first and floating agriculture secured second position in case of advanced proficiency to cope with climate change vulnerability. The research exposed that changes in planting and harvesting dates are the most efficient climate change adaptation strategy practiced by farmers and their experience of agricultural farming strongly influences adaptation choices in the study area. [ABSTRACT FROM AUTHOR]

Sesame is a major annual oil crop that is grown practically everywhere in tropical and subtropical Asia, as well as Africa, for its very nutritious and tasty seeds. Rising temperatures, droughts, floods, desertification, and weather all have a significant impact on agricultural production, particularly in developing countries like Ethiopia. Therefore, the main objective of this study is to examine the influence of climate change on the sesame yield in North Gondar, North Ethiopia, by using the autoregressive distributed Lag (ARDL) time series model. This study employed climate data from the Bahirdar Agrometeorological Center and secondary data on sesame production from the Ethiopian Statistical Service, spanning 36 years, from 1987 to 2023. Autoregressive Distributed LAG (ARDL) includes diagnostic tests for both short- and long-term autoregressive models. The results for the long-run and short-run elastic coefficients show a significant positive association between temperatures and sesame yield. Sesame yield and rainfall have a significant negative long-run and short-run relationship in North Gondar, North Ethiopia. ARDL results confirm that temperature and rainfall have significant effects on sesame productivity. Temperature had a considerable favorable effect on sesamen production, but rainfall had a negative effect in North Gondar, Ethiopia. Based on the evidence acquired from our study, we made several policy recommendations and suggestions to government officials, policymakers, new technologies, researchers, policy development planners, and other stakeholders in order to develop or implement new technology to halt its production and direct adaptation measures in light of the certainty of global warming and the characteristics of climate-dependent agricultural production. [ABSTRACT FROM AUTHOR]

Polarimetric Radio Occultations (PRO) represent an augmentation of the standard Radio Occultation (RO) technique that provides precipitation and clouds vertical information along with the standard thermodynamic products. A combined dataset that contains both the PRO observable and the RO standard retrievals, the resPrf , has been developed with the aim to foster the use of these unique observations and to fully exploit the scientific implication of having information about vertical cloud structures with intrinsically collocated thermodynamic state of the atmosphere. This manuscript describes such dataset and provides detailed information on the processing of the observations. The procedure followed at UCAR to combine both H and V observations to generate the equivalent profiles as in standard RO missions is described in detail, and the obtained refractivity is shown to be of equivalent quality as that from TerraSAR-X. The steps of the processing of the PRO observations are detailed, derived products such as the top-of-the-signal are described, and validation is provided. Furthermore, the dataset contains the simulated ray-trajectories for the PRO observation, and co-located information with global satellite-based precipitation products, such as merged rain rate retrievals or passive microwave observations. These co-locations are used for further validation of the PRO observations and they are also provided within the resPrf profiles for additional use. It is also shown how accounting for external co-located information can improve significantly the effective PRO horizontal resolution, tackling one of the challenges of the technique. [ABSTRACT FROM AUTHOR]

Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models. Plain Language Summary: Accurate risk assessment of extreme weather events in a changing climate requires high‐resolution data, which is expensive for climate models to produce. Thus, there arises a need for downscaling (super‐resolution) methods. We show that by using simplified physics and statistics to prime generative adversarial learning, one can reconstruct observed high‐resolution mid‐latitude extreme rainfall fields from low‐resolution climate models with high fidelity. As a result, uncertainty quantifying downscaling (25:1 ratio) has become feasible, overcoming limitations of extant dynamical and statistical downscaling methods. Key Points: We leverage data, physics, and machine learning to develop an approach to downscale rainfallPriming a two‐step generative adversarial learning with simplified physics and statistics and applying optimal estimation‐based bias correction overcomes common limitations of conventional downscaling strategiesThe proposed downscaling method enables the rapid and robust estimation of extreme rainfall hazards [ABSTRACT FROM AUTHOR]

The potential collapsible deformation of loess surrounding rock is not conducive to the safety and stability of the tunnel, especially under rainfall conditions, which may induce severe engineering disasters. This paper analyzes the regularity of water infiltration in loess and failure response of tunnel structure under rainfall conditions. Rainfall infiltration in loess strata includes unsaturated infiltration and dominant channel infiltration. The dominant channels enhance soil permeability, leading to the overall downward movement of the wetting front, which provides conditions for the instability and failure of surrounding rock of loess tunnel. Through case statistics, the loess tunnel engineering disasters caused by rainfall are analyzed, elucidating the mechanism of tunnel instability and failure. Finally, according to the characteristics of loess surrounding rock and the response regularity of rainfall to tunnel structure, a series of engineering measures to prevent the deformation of surrounding rock and the specific construction method of loess tunnel structure safety warning system under the condition of rainfall are proposed. [ABSTRACT FROM AUTHOR]

The earthquake swarm in Palghar, western India, is a natural laboratory to understand long-duration seismogenesis. However, it requires an accurate estimation of the key parameters, such as earthquake locations and the velocity model of the region. Two separate studies performed by two organisations in India, the CSIR-National Geophysical Research Institute (NGRI) and the National Center for Seismology (NCS) reported significantly differing results from the first 8–12 months of earthquake monitoring in Palghar. CSIR-NGRI reported the depth of earthquakes in the range of 4–15 km, whereas NCS reported a much shallower depth down to 7 km only. We investigate the reason for this depth discrepancy by analysing datasets from the networks of both these organisations. We find that no available velocity models are able to reconcile the depth difference between the two datasets. Selection criteria generally adopted to determine a good velocity model, such as travel-time residuals and location errors, also failed to identify a suitable velocity model. Several synthetic tests helped us to identify that the network geometry and source-station separation are contributing factors to the observed depth difference. However, the main reason for the discrepancy was found to be inconsistent velocity models used for location at both networks. The main objective of our work is to estimate a velocity model which minimises the depth discrepancy between both datasets. In this regard, we synthetically generated hundreds of models with different VP and VP/VS ratios, followed by a grid search of VP and VP/VS ratios, which harmonises the location from the two datasets. The best model consists of a VP lying between 5.55–5.85 km/s and a VP/VS ratio between 1.77–1.81. For this velocity model, there is a greater overlap of the hypocentral clouds from both networks, and about 80% of the earthquakes of the CSIR-NGRI network were located within depths ≤ 7 km. We find that location errors vary seasonally, which is also related to the VP/VS of the crust. Since earlier studies have also found a strong correspondence between rainfall and seismicity in this region, we infer that the high VP/VS ratio might have been caused by the rainfall saturation of cracks in the Palghar seismicity zone. [ABSTRACT FROM AUTHOR]