Average Annual Rainfall Research Articles

Classification Importance of Seed Morphology and Insights on Large-Scale Climate-Driven Strophiole Size Changes in the Iberian Endemic Chasmophytic Genus Petrocoptis (Caryophyllaceae)

Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole’s biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus’ entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis. Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.

Rainfall Patterns and Groundwater Dynamics: Implications for Soil and Water Conservation in Kodihalli Sub-Watershed, Haveri District, Karnataka, India

The Kodihalli sub-watershed (4D4D2f) is lies between 14028’23.68”-14034’20.103” North latitudes and 75026’49.224”-75031’44.208” East longitudes and covers an area of about 4398.83 ha. with an average annual rainfall of 783.0 mm (2014-2021). The maximum rainfall of 439.0 mm is received from June to September, 226.0 mm from October to early December and the remaining 118.0 mm is received during the rest of the year. The number of rainy days (>2.5 mm) varied from 13-24 days per year. On an average, the number of rainy-day events likely to produce runoff (20 to 30 mm) are about 2 to 8 rainy days per year with moderate variation across the years. The Actual Evapotranspiration (AET) over the years 2014 - 2021 in the Kodihalli sub-watershed varied from 383.0 to 770.0 mm. During 2014 - 2021, the average annual AET (621.0 mm) was less than the average rainfall (783.0 mm). The average AET/P ratio between 2014-2021 was about 0.79 which is less than the sustainable limit of about 0.80. The soils of watershed viz., sandy clay loam (IR 10 mm/hr) and sandy clay (IR 14 mm/hr) resulted in more infiltration rate than clay (2 mm/hr) and clay loam soils (7 mm/hr). Soil infiltration during a rainstorm is closely related to a number of factors such as the intensity and kinetic energy of the rainfall, soil surface conditions and soil properties such as those related to aggregate stability. For effective soil and water conservation, maximum area of about 3769 ha (85.7%) requires graded bunding and 267 ha (6.06%) area requires contour bunding.

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Impact of Rainfall and Soil Infiltration on Groundwater Dynamics of Yerekoppi-1 Micro-watershed, Haveri District, Karnataka, India

The amount of water infiltrating the soil surface directly affects the quantity of surface runoff and erosion, and the recharge of both soil and groundwater. Groundwater recharge is an informative indicator of the water located beneath the ground surface. It has a direct impact on renewable supplies of groundwater. In the present study on Impact of rainfall and soil infiltration rate was used for effect on the groundwater level of the Yerekoppi-1 micro watershed (Manakur sub-watershed) in Haveri district of Karnataka state. Average annual rainfall is 700.7 mm and mean potential evapotranspiration is 1541.8 mm. An area of about 888 ha (94.32 %) has clay texture at the surface and 53 ha (5.68%) area has contributing settlement. In Yerekoppi-1 micro- watershed, 38 borewells are there in the vicinity of major stream of the micro- watershed. The mean depth of groundwater levels in the micro- watershed were monitored at a monthly frequency during January-2023 to March-2024. It was found that the maximum average depth of bore wells are about 17.6 mbgl and lowest average depth of borewell about 8.8 mbgl. Soil infiltration rate was measured at upper, middle and lower reaches of the watershed area in the year 2023 and 2024. The majority of the area contributed clay soil. In all the areas of micro-watershed, infiltration rate for clay soil is less than 5 mm/hr. Infiltration rates for all the areas was 1.72- 4.78 mm/hr. Soil infiltration during a rainstorm is closely related to a number of factors such as the intensity and kinetic energy of the rainfall, soil surface conditions and soil properties such as those related to aggregate stability. The groundwater recharge may be improved by growing plantation crop for increasing infiltration rate, construction of percolation tanks and farm ponds in the lower most of the agricultural land.

Direct Seeding of wheat Using Happy Seeder in Paddy Residue Conditions in Madhya Pradesh, India

Trials were conducted on direct sowing of wheat variety GW-322 through happy seeder in combine harvested rice fields in district Narsinghpur, Madhya Pradesh, during two successive year’s viz. 2015-16 and 2016-17. The control treatment was conventional field preparation after combine harvesting of rice followed by sowing of wheat by seed cum fertilizer drill. District Narsinghpur lies in Central Narmada valley region of India. The average annual rainfall of the district is 1136 mm [1] (Yadav et.al. 2021). Soils of the district come under the vertisol category. Results showed that crop yield and net return under the direct sowing of wheat through happy seeder was higher than that obtained under the conventional method. Direct sowing through happy seeder fetched an average crop yield of 47.45 q/ha whereas under the conventional method it was 43.10 q/ha. The net return under direct sowing through happy seeder was Rs. 59025/- per ha whereas it was only Rs. 48850/- per ha under the conventional method. Happy seeder is an innovative technology and is being widely adopted to reduce stubble problem and conserve soil and environment. The results showed that the direct sowing of wheat through happy seeder performed better than conventional field preparation followed by sowing through conventional seed cum fertilizer drill in combine harvested rice fields.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Impact of typical environments in China's western mountainous areas on the durability of railway concrete: a review

PurposeThis study aims to analyze the impact mechanism of typical environments in China's western mountainous areas on the durability of railway concrete and propose measures to improve durability.Design/methodology/approachWith the continuous promotion of infrastructure construction, the focus of China's railway construction has gradually shifted to the western region. The four typical environments of large temperature differences, strong winds and dryness, high cold and low air pressure unique to the western mountainous areas of China have adverse effects on the durability of typical railway structure concrete (bridges, ballastless tracks and tunnels). This study identified the characteristics of four typical environments in the western mountainous areas of China through on-site research. The impact mechanism of the four typical environments on the durability of concrete in different structural parts of railways has been explored through theoretical analysis and experimental research; Finally, a strategy for improving the durability of railway concrete suitable for the western mountainous areas of China was proposed.FindingsThe daily temperature difference in the western mountainous areas of China is more than twice that of the plain region, which will lead to significant temperature deformation and stress in the multi-layered structure of railway ballastless tracks. It will result in cracking. The wind speed in the western plateau region is about 2.5 to 3 times that of the plain region, and the average annual rainfall is only 1/5 of that in the plain region. The drying effect on the surface of casting concrete will significantly accelerate its cracking process, leading to serious durability problems. The environmental temperature in the western mountainous areas of China is generally low, and there are more freeze-thaw cycles, which will increase the risk of freeze-thaw damage to railway concrete. The environmental air pressure in the western plateau region is only 60% of that in the plain region. The moisture inside the concrete is more likely to diffuse into the surrounding environment under the pressure difference, resulting in greater water loss and shrinkage deformation of the concrete in the plateau region. The above four issues will collectively lead to the rapid deterioration of concrete durability in the western plateau region. The corresponding durability improvement suggestions from theoretical research, new technology development and standard system was proposed in this paper.Originality/valueThe research can provide the mechanism of durability degradation of railway concrete in the western mountainous areas of China and corresponding improvement strategies.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Variability of UCP1 and UCP3 uncoupling protein genes in relation to climate in indigenous populations of Siberia and the Far East

Introduction. Several studies have demonstrated a correlation between variations in genes regulating human uncoupling proteins (UCP) and environmental factors. However, information on the intra- and interpopulation diversity of allele and genotype frequencies of UCP1 and UCP3 within the territory of Russia remains insufficient. This study aims to investigate the intra- and inter-ethnic variability of genetic determinants of activity of uncoupling proteins UCP1 and UCP3, and to evaluate genotype-environment associations in the populations residing in the northeastern region of Russia. Materials and methods. Genotyping was conducted on a total of 1,698 biological specimens collected from individuals representing 22 population groups of European Russia, Siberia, and the Russian Far East. Geographical and climatic data spanning from 1940 to 2023 were gathered for each sample collection locality. Results and discussion. The spatial distribution of genetic determinants affecting the activity of UCP1 and UCP3 uncoupling proteins demonstrates a correlation with the severity of natural conditions. Three regression models of allele frequencies were constructed using climatic characteristics as predictors. All models are statistically significant (p<0.05 in all cases) and explain 39%, 36%, and 64% of the variability in UCP1 (rs6536991, rs1800592) and UCP3 (rs1800849) allele frequencies, respectively. These correlations revealed by the models confirm the adaptability of UCP genes in the indigenous population groups of Northern Eurasia. Conclusion. The results of this study are consistent with the assumptions reported in scientific literature and significantly enhance existing knowledge. We observed correlations between allele and genotype frequencies of UCP1 (rs6536991, rs1800592) and UCP3 (rs1800849) genes and geographic latitude, elevation above sea level, as well as climatic indicators such as annual average rainfall, range of surface air temperatures, and the Bodman “weather severity” index. Further investigation involving a larger number of ethnic and territorial groups is necessary to better understand the factors influencing UCP gene variability formation.

Accounting of weathering enhancement by mafic mineral spreading for CO 2 storage

The reduction of atmospheric CO 2 is an urgent global issue in the mitigation of global warming. Carbonate acts as a carbon sink in Earth's surface layer, and the weathering of terrestrial silicates and the deposition of oceanic calcium carbonate and organic matter mainly determine atmospheric and oceanic carbon dynamics on geological time scales. Various CO 2 reduction approaches have been evaluated, and the weathering acceleration method is expected to be a low-cost approach. This study attempts to estimate carbonate precipitation potential and dissolution rate from alkali leaching rates using weathering tests on mafic rocks. Based on Japan's annual average rainfall, the in situ carbonate precipitation potential obtained by spreading an olivine mineral or basalt rock powder at 40 ton-powder/ha was on the order of 10 -3 ton/year/ton-rock. This weathering rate could not be greatly accelerated under natural temperature conditions and fine granulation of the minerals.

Rainfall And Drought Characteristics For Crop Planning In Cuttack District Of Odishastate, India

The increasing frequency of agricultural droughts and variability in rainfall patterns over the past three decades prompted this study, which analyzed 34 years of block-wise daily rainfall data (1988-2021) from Cuttack district, Odisha. The aim was to determine the long-term average and temporal variability of rainfall on weekly, monthly, seasonal, and annual scales, with a focus on identifying strategies to mitigate production risks in the region. The average annual rainfall in Cuttack was 1597.6 mm, with August receiving the highest amount of rainfall (476 mm), followed by July (407 mm) during the monsoon season. A trend analysis on rainfall over the past 34 years showed an increasing pattern of an average 6.03 mm and 3.6 mm per year in annual and kharif season rainfall, respectively. Agricultural droughts were frequently observed during the early (22-28 Standard Meteorological Week) and late (36-42 SMW) stages of kharif crops. The study also revealed that the station is prone to mild-moderate meteorological droughts of different intensities, including mild,moderate, and severe. To minimize production risks, short duration, low water-requiring, high-value crops such as maize, pulses, oilseeds, and some vegetables are recommended for this region.

Spatio-temporal variability of precipitation from 1980 to 2021 within the Oum Er Rbia hydraulic basin (Morocco)

Abstract The aim of this work is to assess the spatio-temporal variability of the mean annual rainfall during the last four decades within the Oum Er Rbia hydraulic basin. The used database in this study consists with a series of rainfall data at different time scale. These data were subjected to statistical and cartographic analysis. The obtained results show that, during the period of 1980-2021, interannual variability marked the evolution of annual rainfall averages. Furthermore, this variability is characterized by alternating sequences of dry and wet seasons. From the hydrological year of 2015, a general decreasing trend of rainfall has clearly marked and observed. Rainfall patterns in the Oum Er Rbia basin are also characterized by spatial variability. Interannual averages vary considerably from one catchment to another. The upstream part of the basin receives the most amount of rainfall. By contrast, the middle of the basin is the driest. The recent drop in precipitation has had a negative impact on the basin’s territory, most notably on water resources and agriculture.

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia.

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

Delineation of fresh groundwater potentiality zones in saline coastal aquifers, Southwest Bangladesh using remote sensing and GIS approaches.

Bay of Bengal in southern Bangladesh is a major source of water from coastal aquifers, but prone to contamination by seawater intrusion,making climate-vulnerable populations and economies unfit for potable, agricultural water, adopting crops, etc. The study area located in Khulna district lies in the southwestern coast of the country is among the most vulnerable due to its salinity issues. Therefore, this study identified fresh groundwater potential zones in the southwestern coastal zones of the country suited for community usage helping coastal peoples meet their demands. This study focused on twelve thematic layers employing remote sensing and GIS with analytical hierarchy process. Here, groundwater salinity is 84% brackish to saline over 70% of the region [electric conductivity: 295-16,295 micro-Siemens/cm]. However, groundwater chloride in 88% signifies a slightly to medium salty zone. The annual average rainfall reduced surface water infiltration in 75% of the area with little to very slightly soil salinity. The fresh groundwater resource zone has classified based on its potentiality as: very high (0.52%)-for drinking, agricultural irrigation, or industrial work; high (25%)-marginal salinity suitable for agricultural or industrial uses; low (11%)-low salinity, but usable for high salt tolerant crops in irrigation; and very low (64%)-very high groundwater salinity, and not suitable for human consumption and community uses. Finally, this study will help develop sustainable groundwater resources in the coastal region and a fresh groundwater supply plan in saline-prone areas.

Improving the water table fluctuation method to estimate groundwater recharge below thick vadose zones

ABSTRACT The water table fluctuation (WTF) method is popular for groundwater recharge (GR) estimation, but its accuracy is challenged when applied in areas with thick vadose zones because of the signal lag and attenuation with depth and uncertainties from barometric pressure effect and lateral flow. Improvement of the WTF method used the linear regression method and Darcy’s law, and has been assessed to give satisfactory results. In particular, the improved method presented lower GR (20–34%) relative to the conventional method. GR decreased from the centre to the edge of the tableland. The regional average GR was 63–81 mm year−1, equivalent to 11–14% of annual average rainfall. Lag times between recharge and rainfall ranged from 1 to 9 months. Rainfall and vegetation dominated the spatiotemporal variability of GR. Our study provides reference and technical support for GR estimation with the WTF method in regions with a thick vadose zone.

Time Series Analysis to Estimate the Volume of Drinking Water Consumption in the City of Meoqui, Chihuahua, Mexico

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km2. The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m3 per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)12 model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m3 (13 ± 93). A SARIMA (2,1,0)(2,0,0)12 model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m3 and exhibited an SARIMA (0,1,1)(2,0,0)12 model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

From crisis to opportunity: climate change benefits livestock production in Somalia

Abstract While livelihoods of Somalian livestock smallholders rely heavily on seasonal climate conditions, little is known of long-term implications of the changing climate for this nation. Here, we quantify implications of the changing climate on the productivity and profitability of livestock smallholders across a rainfall gradient in northwestern Somalia. Using the Sustainable Grazing Systems (SGS) model, we explore 80 future climate realisations, with global climate model projections including low- and high-impact socio-economic pathways (SSP245 and SSP585), two climate horizons (2040 and 2080) and four case study farm regions. In general, future seasonal and annual rainfall and temperature relative to the baseline period (1981–2020) increased for most regions. Mean annual temperatures increased by 9%–14%, while cumulative annual precipitation increased by 37%–57% from mid to late century, respectively. Grassland production increased with later climate horizons, as higher average annual rainfall together with elevated atmospheric carbon dioxide drove up growth rates in spring and autumn. Under the low emissions scenario (SSP245), changes in farm profit were modest or positive, ranging from negative 4% in Berbera–20% plus in Sheikh. Under the higher emissions scenario (SSP585), farm profits were higher, ranging from 23% to 42% above baseline profits, largely due to greater pasture production and lower requirements for supplementary feed. We conclude that future climates will benefit the productivity and profitability of smallholder farmers in Somalia, although more agile farm management will be required to cope with increased seasonal climate variability.

Arachnid Assemblage Composition Diverge between South- and North-Facing Slopes in a Levantine Microgeographic Site

Local microgeographic sites subdivided by sharp ecological and climatic contrasts are important platforms for measuring biodiversity patterns and inferring the possible effect of climatic and ecological variables on species distributions and habitat use. Here, we report results from 24 months (September 2019–August 2021) of continuous pitfall trapping collection in Lower Nahal Keziv, Western Upper Galilee, Israel (“Evolution Canyon” II (hereafter—EC II)). This site receives an average annual rainfall of 784 mm and contains two slopes that differ markedly by solar radiation and plant formation. The first is the south-facing slope (SFS), which is characterized as a semiarid garrigue and open grassland. The second is the contrasting north-facing slope (NFS), which is characterized by a more humid East Mediterranean forest. The slopes are separated by a narrow valley bottom (VB). Analysis of ca. 1750 arachnid specimens, collected from 70 pitfall traps along the slopes and valley, indicates significantly different arachnid assemblages between the NFS and SFS, likely due to the differences in solar radiation that affect plant-cover percentage, which in turn affects the arachnid assemblage composition. In addition to 98 arachnid taxa collected and identified to species and morphospecies level, this study resulted in the discovery of two species new to science, which are described as part of this publication (100 arachnid species and 11 additional taxa that were not identified to species, a total of 111 taxa). Our study, moreover, contributes new ecological data on the spatial and temporal distribution of arachnids, and therefore attests to the importance of year-round sampling in an understudied region. Overall, our study enables a better understanding of arachnid diversity and their distributions and serves as a reference for future research aimed at testing the effect of climate change and other environmental factors that influence arachnid assemblages in natural habitats.

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

Assessment of crop suitability analysis using AHP-TOPSIS and geospatial techniques: A case study of Krishnagiri District, India

The cropped area of Krishnagiri district is about 37 % of the total geographical area, and due to the utilization of non-agricultural purposes (8.2% of agricultural land) it is mandatory to increase the agricultural production to feed the people. So, this study has been planned to find the agricultural suitability and crop suitability of the Krishnagiri district using AHP-TOPSIS and GIS techniques. In this study, three main limiting factors, such as climate, soil, and topographical parameters (Annual average rainfall, Annual average temperature, slope, pH, Electrical conductivity (EC), Soil organic carbon (SOC), available NPK, and soil texture), were used as criteria for the suitability of crops. All criteria maps were generated using Arc info 10.4 software. The AHP used to estimate the weight and TOPSIS (Technique for Order of Preference by Similarity to an Ideal Solution) approach was assign the rank of each criterion for the suitability analysis. Based on the relative closeness to the ideal solution(pi), the generated crop suitability maps were categorized into five classes, namely “highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N)”. The results revealed that, crop suitability maps finds that that maize, field bean, cluster bean, groundnut, pomegranate, lemon, and lemongrass were highly suitable for the Krishnagiri district. Rice, cotton, sugarcane, sunflower, and cashewnut were moderately suitable, and vanilla was unsuitable. This study will guide farm managers and land policymakers in making decisions and assist farmers in selecting crops that are highly suitable for achieving potential yield and profit.