Annual Rainfall Research Articles

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.

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Grass (Poa annua L.) cover for eight years as an effective strategy for recovering soil moisture

Deep soil desiccation has become a major obstacle to restoring vegetation in the Loess Plateau region of China. The restoration of deep soil water plays an important role in ecosystem health. In the present study, we compared the effects on moisture variations in deep dry soil of grass cover and clean tillage treatments based on in-situ soil column monitoring for eight years. The results showed that the dry soil layers could temporarily disappear and reappear again with rainfall under clean tillage. However, grass cover effectively increased the deep soil moisture. Grass primarily consumed soil moisture from the shallow layer (0–2 m), and the total amount of water consumed during the whole growth period was less than the annual rainfall in dry years. The average soil desiccation index decreased by 24.20 % and the deep soil water moisture (2–10 m) recovered from 7.5 % to 12.8 % after eight years, with an annual average recovery of 0.66 %. Thus, it would take half a year to recover to the stable field capacity and 14 years to reach the field capacity. Recovery of the soil moisture also depended on the rainfall supply. We found that precipitation had an obvious lagged effect of approximately two years on the supply of deep soil moisture. Our findings contribute to understanding how to alleviate dry soil layer, with potentially helpful implications for the ecological health in arid and semiarid regions.

Optimal groundwater depth and irrigation amount can mitigate secondary salinization in water-saving irrigated areas in arid regions

Secondary salinization poses a significant threat to the sustainable development of water-saving irrigation districts. This study aims to explore the spatial and temporal variations in soil salinity and the factors influencing these changes in water-saving irrigation areas in the inland arid regions of Northwest China. The Manas River Irrigation District was selected as the study area. A grid measuring 10 km × 14 km grid was designed to determine the latitude and longitude coordinates of the grid centers, resulting in 66 sample points. Soil samples were collected from these points in 2013, 2014, 2020, and 2021 from the 0100 cm layer to obtain salinity data. Based on existing research and practical conditions in water-saving irrigation areas, 11 factors influencing soil salinity changes were identified, including irrigation area and irrigation amount. Classical statistical methods and interpretable machine learning techniques were employed to analyze the distribution characteristics of soil salinity and the influencing factors. This analysis proposes effective solutions to mitigate potential secondary salinization in irrigation areas. The results revealed that soil salinity in the irrigation area belonged to moderate variation (Cv = 46.74 %51.80 %). The horizontal direction of the irrigation area shows higher salt content in the upstream and downstream areas, and a gradual decrease in variability with increasing depth characterizes the vertical direction. From 2013–2021, soil salinization in the irrigation area gradually decreased. In 2013 and 2014, the area was predominantly covered by mild saline-alkali soil, accounting for 75.1 % and 76.6 % of the total area, respectively. However, in 2020 and 2021, non-saline soils became dominant, covering 60.9 % and 66.5 % of the total irrigation area, respectively. In order of importance, the factors affecting the spatial and temporal evolution of soil salinity are groundwater depth, annual water surface evaporation, water-saving irrigation area, underground water diversion amount, mineralization of groundwater, irrigation amount, surface water diversion amount, and annual rainfall. In the oasis irrigation area, maintaining a groundwater depth of 4.06.0 m and an irrigation amount of 55006000 m3 ha−1 can alleviate the problem of secondary salinization that may result from large-scale development of water-saving irrigation. The findings of this study provide a basis for the prevention and control of soil salinization in water-saving irrigation areas and the development and management of saline land in oasis areas.

EFFECT OF HERBIVORY ON THE REFORESTATION OF FOUR SPECIES OF THE FABACEAE FAMILY IN DETERIORATED AREAS

Reforestation plays an important role in attempting to reverse ecosystem degradation in arid regions, where natural rainfall variability makes ecological restoration difficult. The aim of this work was to evaluate the survival and growth of four native species of the Fabaceae family (Prosopis velutina Wooton, Havardia mexicana (Rose) Britton & Rose, Parkinsonia microphylla Torr., and Olneya tesota A. Gray), with and without protection against herbivory in a degraded arid land in Sonora, Mexico. A three-level factorial design was established according to the protection treatments (PET and mesh, 20 and 30 cm in height, respectively) and a control (without protection). The variables of survival and height were evaluated 6, 12, and 24 months after transplant. Computer packages were used for the statistical analyses, establishing 5 % significance. Survival was highest in the plants with protection, with 100 % for the first year, both for PET and mesh, in comparison with a survival rate of 16.6 % for the unprotected control plants. The second year, the highest survival rate was found in plants protected by a mesh (41.6 %), whereas plants protected with PET and the control had the lowest survival rates, both with 8.3 % (χ2 = 5.674, p ≤ 0.058). The height of plants was affected by herbivory. Plants protected by mesh reached a greater height (29.4 cm) in comparison with the PET treatment, with 17.4 cm, and the control, with 3.1 cm (p < 0.0001). The growth of native forage fabaceae (leguminous) tree species in Sonora is extremely slow and relies on both herbivore protection and above-average rainfall years. This work presents the use of low-cost herbivory protection strategies to increase the survival and height of plants 2 years after being established.

Annual and Interannual Rainfall Variability in Indonesia Using Empirical Orthogonal Function (EOF) Analysis and Its Response to Ocean-Atmosphere Dynamics

We investigate rainfall variability in Indonesia using the Empirical Orthogonal Function (EOF) method. The analysis starts by taking three main modes of EOF results, namely EOF1, EOF2, and EOF3. The EOF1 region is southern Indonesia, from southern Sumatra to Timor Island, parts of Kalimantan, parts of Sulawesi, and parts of Irian Jaya. The EOF2 region is located in northwestern Indonesia and includes the northern part of Sumatra and the northwestern part of Kalimantan. The EOF3 region covers Maluku. This study aims to analyze the annual and inter-annual variability of rainfall in anticipation of the threat of hydrometeorological disasters. Based on the correlation value of the principal component (PC) with the dipole mode index (DMI) and Niño3.4 index, it has a period similar to El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Rainfall in Indonesia is very sensitive to sea surface temperature (SST) in the southeastern Indian Ocean and the central Pacific Ocean, which means that rainfall patterns in Indonesia can change significantly if SST in the region changes.

Effects of Long-Term Application of Nitrogen Fertilizer on Soil Acidification and Biological Properties in China: A Meta-Analysis.

Soil acidification is a global environmental problem with significant impacts on agricultural production, environmental protection, and ecosystem health. Soil acidification is widespread in China, affecting crop yields, agricultural product quality, and biodiversity. Since the 1980s, much work has been done on acidic soils in China, but it is controversial whether excessive nitrogen fertilizer application can lead to soil acidification mechanisms. To address the above issues, we conducted a meta-analysis of 115 published papers to integrate and analyze the effects of N fertilizer application on soil acidification and biological properties from 1980 to 2024. We also quantified the effect of nitrogen fertilization on soil acidification and biological changes under different climatic conditions. The results showed that under long-term application of nitrogen fertilizers in China from 1980 to 2024, soil pH decreased by an average of 15.27%, and the activities of soil urease, nitrate reductase, nitrite reductase, catalase, glutamate dehydrogenase, and glutamate synthetase decreased by an average of 9.82-22.37%. The soil microbial community richness (Chao1 index) increased by 6.53%, but the community diversity (Shannon index) decreased by 15.42%. Among the dominant soil microorganisms, the relative abundance of bacteria decreased by an average of 9.67-29.38% and the abundance of gene expression of nifH, amoA-AOA, amoA-AOB, and qnorB decreased by 9.92-19.83%. In addition, we found that the mean annual temperature and rainfall impacted soil acidification via their effect on soil microbial diversity and community composition. This study provides a scientific basis for an in-depth understanding of the spatial and temporal variation of soil acidification and biological properties in China.

Freeze-thaw landslide susceptibility assessment and its future development on the seasonally frozen ground of the Qinghai-Tibet Plateau under warming-humidifying climate

Exploring freeze-thaw landslide susceptibility on the Qinghai-Tibet Plateau (QTP) under warming-humidifying climate is greatly important for preventing and mitigating the risks of landslide hazards on engineering facilities. This study proposed a random forest-based freeze-thaw landslide susceptibility assessment model, where annual rainfall, annual average air temperature (AAAT), slope gradient, normalized difference vegetation index (NDVI), elevation, lithology, and plan curvature were fully considered. Selecting a study area of 324 km2 on the seasonally frozen ground (SFG) of QTP with 1059 freeze-thaw landslides, the model accuracy was validated. Low, moderate, high, and very high susceptibility zones were precisely classified, which accounted for 27.0, 27.5, 28.3, and 17.2%, respectively. Furthermore, its future development was explored under warming, humidifying, and warming-humidifying climates. Results indicated that when the AAAT or annual rainfall increased by 1.16 °C or 20 mm, both high and very high susceptibility zones increased by 2.0 or 1.0%, respectively. When AAAT and annual rainfall simultaneously increased by 1.16 °C and 20 mm, a higher increase in the high and very high susceptibility zones of 2.8% occurred. It was noteworthy that climate warming transitioned low and moderate susceptibility zones into high and very high susceptibility zones. These areas where freeze-thaw landslide susceptibility changed featured the AAAT of 4.29–6.15 °C, annual rainfall of 528.9–552.3 mm, slope gradient of 16–25°, and elevation of 3750-3940 m. Compared to climate warming, the humidifying climate and warming-humidifying climate expanded moderate susceptibility zones, and areas where freeze-thaw landslide susceptibility changed featured the gentler slope gradients of 8–16°. This study can provide a better guidance for safe engineering constructions influenced by freeze-thaw landslides on the QTP.

Study on the ecological environment quality and its driving factors of the spatial transformation of production-living-ecological space in Baishan City

In order to strengthen the overall planning and coordination of urban construction and ecological space in Baishan City, we should formulate a scientific land type planning of Production-Living-Ecological Space (PLES). In this paper, land use dynamic attitude model, land use transfer matrix, land use function center of gravity transfer model, eco-environmental quality index(EEQI) and geographic detector model are used to discuss the spatio-temporal evolution of PLES in Baishan City from 2000 to 2020. Spatio-temporal evolution of EEQI and its influencing factors. The results show that: (1) During the study period, Baishan City showed the characteristics that the production space first increased and then decreased, the ecological space decreased, and the living space continued to increase. Among them, the ecological space is the dominant space of Baishan City, covering an area of more than 80%. From the perspective of the transformation of PLES, from 2000 to 2020, Baishan City is mainly characterized by the transformation of ecological space into production space and living space. In the second type of space, green ecological space, forestry ecological space and other ecological space have decreased, while other types of space have increased in varying degrees. (2) During the study period, the overall EEQI of Baishan City remained in good condition, and the ecological environment quality(EEQ) of the three periods were 0.6571, 0.6412 and 0.6562 respectively. The higher EEQI is distributed in Changbai County and Linjiang City, while the areas with lower EEQI are concentrated in the north-central part of Hunjiang City, the middle part of Jiangyuan District and the northwest of Fusong County. (3) According to the analysis of the influencing factors of EEQ, the influence of the factors of spatial differentiation of EEQI in Baishan City changed significantly from 2000 to 2020, and the average annual rainfall was the core factor affecting the spatial differentiation of EEQ in Baishan City, the second is the urbanization rate and the distance from the county government, and the interaction between the average annual rainfall and the distance from the county government has a strong influence on the spatial differentiation of EEQ in Baishan City. This study reveals the evolution of spatial types and EEQ of PLES in Baishan City, and provides a scientific reference for the effective management and utilization of land resources in Baishan City.

A macro-level life cycle environmental-economic impact and benefit assessment of sponge cities in China

Urban runoff source control facilities (URSCFs) are integral components of Sponge City (SC), playing a pivotal role in providing ecosystem services and managing water quality and quantity. To accurately assess the performance of URSCFs, it is crucial to quantify their environmental and economic impacts. However, previous studies have predominantly focused on location-specific case studies, lacking a macro-level perspective necessary for informing public policy development pertaining to SC initiatives. This study aims to bridge this gap by conducting a macro-level life cycle assessment across China's 16 initial pilot SCs, categorized into three zones based on their annual rainfall volume control rate α (i.e., Zone II (80 % to 85 % for α), Zone III (75 % to 85 % for α), and Zone IV (70 % to 85 % for α)). By simulating five rainfall drainage scenarios, we delve into the environmental benefits of SC construction. The results indicate that Zone III incurs the highest environmental and economic costs during the construction phase, followed by Zones II and IV. In Zone III, bioretention contribute significantly to the environmental impact and economic cost, while constructed wetland and detention cells are the key contributors for Zone II. During the operation phase, Zone III demonstrates the largest environmental and economic benefits, with Zone IV and II trailing behind. The investment payback period for SCs in all zones is less than eight years, with Zone IV recovering costs the fastest (3.9 years) and Zone II the slowest (7.5 years). Facilities like detention cells, green roofs, and permeable pavements tend to have longer payback periods. Based on our findings, we recommend that Zone II exercise caution in constructing detention cells, permeable pavements, and wetlands, while Zone III should carefully consider green roofs and bioretention to optimize SC investments. Due to the fewer URSCFs constructed in Zone IV, it exhibits the lowest environmental impact compared to Zones II and III. Our research provides valuable insights to support policymaking with regards to future SC planning and development.

Can blue–green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment

Abstract Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue–green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

Integrated machine learning and geospatial analysis enhanced gully erosion susceptibility modeling in the Erer watershed in Eastern Ethiopia

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

Generalized fiducial inference for the GEV change-point model

Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we propose a change-point detection procedure for GEV distribution based on generalized fiducial inference. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis–Hastings algorithm is utilised to estimate the location of the change point and its confidence interval. In addition, the generalized fiducial factor is used to test whether there is a change point. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, we apply the proposed method to the annual maximum rainfall data in Beijing.

Critical climate-stress moments for semi-arid farming systems in India

In the face of the increasing frequency of climate stresses, climate change projections can help in adaptation planning and resilience-building. However, typical climate change projections, such as annual average rainfall and temperature increases, are not helpful for farmers in understanding specific climate risks, like crop loss, and making adaptation decisions. Our study aims to identify and characterise context- and time-specific climate stresses in terms of climate conditions of concern to improve the understanding of future climate risks and enhance the climate resilience of semi-arid farming systems in India. Utilising the concept of critical climate-stress moments, we employ an innovative bottom-up methodology integrating insights from focus group discussions with farmers, key informant interviews, and an ensemble of downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP-6) models. Our case studies include (i) a mixed crop-livestock farming system, (ii) a horticulture-based farming system, (iii) a cash crop–dominant farming system, and (iv) a cereal-dominant farming system. The specific climate conditions of concern identified were (i) increasing volume of late-monsoon rainfall, (ii) rising winter temperatures, (iii) increase in the number of days with temperatures exceeding 40 °C, (iv) increase in days with heavy rainfall (> 25 mm), and (v) increasing rainfall during the dry season. Identifying these critical moments improves understanding of both the temporal and spatial variations in climate risks, providing valuable inputs for targeted and implementable climate resilience–building actions. We recommend revising national and state action plans on climate change by utilising such region-specific assessments of critical climate-stress moments.

Advancing the LightGBM approach with three novel nature-inspired optimizers for predicting wildfire susceptibility in Kauaʻi and Molokaʻi Islands, Hawaii

This study developed three hybrid light gradient boosting machine (LightGBM) models using novel metaheuristic algorithms (golden jackal optimization [GJO], the pelican optimization algorithm [POA], and the zebra optimization algorithm [ZOA]) to predict wildfire susceptibility on Kauaʻi and Molokaʻi islands, Hawaii. Thirteen geo-environmental variables were employed as potentially influential variables to predict wildfire susceptibility, while 1641 and 136 recorded wildfire ignition points on Kauaʻi and Molokaʻi islands, respectively, and 1641 and 136 randomly generated non-wildfire locations on the same islands were used as output data. The impact of the independent variables on wildfire susceptibility was interpreted using the Shapley additive explanations (SHAP) method. It was found that ZOA-LightGBM had the highest accuracy (AUC = 0.9314) for the prediction of wildfire susceptibility on Kaua‘i island, followed by GJO-LightGBM (0.9308), POA-LightGBM (0.9303), and LightGBM (0.9228). On Molokaʻi island, ZOA-LightGBM also achieved the highest AUC (0.858), outperforming POA-LightGBM (0.8488), GJO-LightGBM (0.8426), and LightGBM (0.8395). Analysis of the hybrid models revealed that the use of metaheuristic algorithms resulted in the setting of the optimal hyperparameters for the LightGBM model, enhancing its performance in modeling wildfire susceptibility on both islands. The SHAP results indicated that the distance from roads, elevation, land surface temperature, and average annual wind speed had the strongest impact on wildfire susceptibility on Kauaʻi Island, whereas distance from roads, slope, and average annual rainfall were the most important variables on Molokaʻi island.

Comprehensive climatic variability analysis and its significance on flood occurrences in the Middle Ganga Plain, India

Climate variability significantly affects rainfall, air temperature, relative humidity, and solar radiation. In subtropical regions like India, intense monsoon rainfall primarily causes floods. This study aims to analyze the climatic factors contributing to flood occurrence in the Middle Ganga Plain (MGP) by examining meteorological parameters through various analyses, including monthly and yearly trends, spatial variability, exceedance probability, historical highest intensity rainfall, and wind trajectory analysis. By establishing a strong link between climatic variables and flood events, this research provides valuable insights into the impact of climate variability on flood patterns in the MGP. Results indicate that monsoon rainfall peaks from July to October, correlating with major flood events. During these months, high relative humidity increases the likelihood of rainfall. Spatial analysis shows higher rainfall in the northern Middle Ganga Plain (MGP), which aligns with observed flood patterns. Trend detection tests reveal increasing annual rainfall and rising average temperatures. Geostatistical analysis identifies the western and eastern MGP as areas prone to extreme rainfall. Exceedance probability analysis confirms the occurrence of substantial monsoon rainfall events. Wind trajectory analysis uncovers seasonal shifts that lead to low-pressure systems and heavy monsoon rainfall. The study establishes a strong link between climatic variables and flood events in the MGP region.

Fine‐scale phenotypic variation of a large herbivore in a pulsed environment

AbstractThe resource rule hypothesis predicts that geographic differences in body size among populations of organisms are due to the amount, availability, and quality of food resources. For instance, the body size of large herbivores is often correlated with soil characteristics because better soils produce better forage. In semiarid environments, rainfall variation is an important driver of forage availability, especially highly nutritious annual forbs. Thus, in such pulsed‐resource environments, it is unclear whether the body size of large herbivores is influenced by fixed resources correlated with soil characteristics, irregular resource pulses correlated with rainfall, or both. Furthermore, it is not clear whether phenotypic expression is a function of forage quality or quantity. During the early autumns of 2011–2018, we captured 4554 white‐tailed deer (Odocoileus virginianus) on seven rangeland sites in the semiarid climate of South Texas, USA. The sites range from coastal to 140 km inland and represent gradients in both soil texture and annual rainfall. We recorded age‐ and sex‐specific indices of skeletal size, antler size, and body mass. Site‐specific soil characteristics explained most of the variation in skeletal size; percent sand was inversely related to skeletal size. For environmentally sensitive phenotypes (antler size and body mass), both soil characteristics and rainfall were influencers; increases in rainfall reduced the negative effect of sand. Percent sand and rainfall were positively correlated with annual biomass of preferred forbs, yet all phenotypic traits declined with increases in forb quantity. Increases in percent shrub cover increased all phenotype sizes. Our data suggest that the phenotypic expression of large herbivores in semiarid environments is driven by forage quality via edaphic characteristics rather than forage quantity via rainfall. Specifically, less sand in the soil allows for the development of shrub communities, which in turn provide a consistent source of forage in a variable, pulsed‐rainfall environment. Although forbs are of higher quality, they are highly ephemeral. The availability of a consistent source of forage may enable white‐tailed deer to extend time invested in body growth, which results in greater phenotype size. Our findings align with the resource rule hypothesis that identifies resource availability as a fundamental element explaining geographical variation in phenotypic expression.

TRENDS AND SPATIAL VARIABILITY OF CLIMATE CHANGE IN NIGERIA’S COASTAL REGION

A major challenge facing the Coastal Region of Nigeria is climate change and climate variability. An assessment of the trends and spatial variability of climate, critical for knowledge-based strategies needed for planning adequate mitigation and adaptation measures for the region, requires urgent attention. This study analysed spatial variability and temporary trends of climate change in the Nigeria Coastal Region .. Gridded monthly climatic data from the Climate Research Unit (CRU 0.5 x 0.5) for two climatic periods ((1956- 1986 and 1987-2016) were obtained. Tests for trend detection and magnitude determination utilized: Mann-Kendal and Linear Regression—Thei-Sen Slope test. Climatic maps for rainfall and temperature from 1956 to 2016 for the coastal regions of Nigeria were developed by ordinary kriging. The results from statistical analyses suggest that there is very high rainfall variability with the coefficient of variability (CV) values ranging between 62.43% and 69.46 % and low-temperature variability with CV values ranging between 4.314% and 6.037%. The annual rainfall and temperature increased at the average rate of 3.047 mm yr-1. and 0.0126 oC yr-1 respectively. In 75% of the region, rainfall was decreasing, while in 25%, it was increasing. The implementation of adaptation and mitigation strategies should be mandatory due to the notable increase in temperature.

Modeling Spatio-Temporal Rainfall Distribution in Beni–Irumu, Democratic Republic of Congo: Insights from CHIRPS and CMIP6 under the SSP5-8.5 Scenario

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha.

Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors of Vegetation Net Primary Productivity in Shendong Mining Area

Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m2. The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m2; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation.