Pygmy mammoths (Mammuthus exilis) and Columbian mammoths (Mammuthus columbi) coexisted on the island of Santarosae (now the Northern Channel Islands of California) until the Late Pleistocene megafaunal extinctions, but the ecology of these mammoths is not yet well explored. In this study, we reconstructed the diets and environments of Late Pleistocene pygmy and Columbian mammoths using stable isotopes in tooth enamel samples from the Northern Channel Islands and Rancho La Brea. The enamel δ13C values indicate that these mammoths primarily consumed C3 vegetation. However, a few individuals consumed significant amounts of C4 plants, CAM plants, or water-stressed woody C3 plants. The mean diet-δ13C value for mainland mammoths (−24.2 ± 1.4‰) is about 2‰ higher than that of island mammoths (−26.4 ± 1.9‰), suggesting that most mainland mammoths consumed either water-stressed C3 vegetation, or some C4 and/or CAM plants. Reconstructed δ18O values of paleo-water from the mainland are generally lower than the mean δ18O values of modern precipitation in Southern California, suggesting conditions were wetter and/or cooler than today. Reconstructed δ18O values of paleo-water from the islands are more similar to modern precipitation. δ13C-based estimates of mean annual precipitation range from 159 to 1407 mm/yr on the islands and from 28 to 387 mm/yr on the mainland. However, consumption of small amounts of C4 and/or CAM plants may have resulted in an underestimation of precipitation for the mainland. Radiometric dating of additional fossils from both localities will help clarify the links between climate change and mammoth evolution and extinction in the region.

The purpose of the article is to verify the applicability to small-scale pile models of the generally accepted methodology for calculating the precipitation value for piles used during construction work. The article discusses and analyzes the existing methods for determining the value of pile precipitation. A brief description of methods using semi-empirical dependencies is given, without taking into account the laws of soil mechanics, engineering calculation methods based on the principles of soil mechanics, as well as numerical calculation methods based on the principles of mechanics. To solve the problem of experimentally determining the precipitation value, an experiment was conducted on models of abrasive piles made in the form of a metal rod. The studies were carried out in deep, finely dispersed loam, which was in a frozen state. Gravel of small fractions, sand, cement and salt were used as the material for pouring concrete. Temperature control at various depths of the ground was carried out by a weather station with an external sensor using a special temperature measuring well. After applying a three-stage static load to the pile models, the dependences of the vertical draft value of the pile models on the value of the static pressure load were obtained. Then, a theoretical calculation of the precipitation values for the selected pile models was carried out according to SP 24.13330.2011 and a comparison of the obtained theoretical and experimental values was carried out.

The Grijalva basin is of great relevance in southern Mexico because it receives the highest precipitation and has the most extensive hydroelectric system in the country. In addition, the lower basin has been impacted by extreme flooding in recent years. It is the source of water for several million people and the regional industry. For this study, the basin was divided into four sub-basins: Angostura, Chicoasén, Malpaso, and Peñitas. Each of these sub-basins has a dam that helps regulate the water flow and generate hydroelectric energy. To better understand the region’s climatology, this study uses long-term rainfall observations from sub-basins to describe precipitation patterns and their variability. Various statistics are computed to describe the annual precipitation cycle for each sub-basin. The results show that Angostura, Chicoasén, and Malpaso share a common climatology, with precipitation peaks in June and September and a mid-summer drought (MSD) in July. Peñitas receives considerably more precipitation throughout the year, with the highest values in October-November. In all sub-basins, La Niña (El Niño) years are characterized by increased (decreased) precipitation in the rainy season. The study demonstrates that the extreme precipitation observed during La Niña years in late summer and autumn is mainly due to an increased number of tropical cyclones over the western Caribbean Sea and the Gulf of Mexico. The interquartile range and other percentile values of monthly precipitation provide additional information that may be useful for dam management.

Abstract Geostatistical applications generally require the calculation of an experimental variogram and the fitting to it of a theoretical variogram model to describe and quantify spatial variation of the whole study area. Since in mountainous areas, it is not always reasonable to assume that a global variogram of the entire study area is suitable for modelling the spatial variation of precipitation, a solution could be using local parameters of the variogram. This study was aimed at determining whether the local geostatistics approach with varying variogram model parameters improves the modelling of average annual precipitation compared to the use of a global variogram with constant parameters for the entire study area. The study area was a region in southern Italy (Calabria) with a spatially variable Mediterranean climate. A dataset of 84 precipitation stations with long-term records (1951–2022) was used. A global variogram model of the precipitation data was calculated and fitted and used in the optimization of the local variograms parameters. Global and local variograms were used with block kriging with elevation as external drift to improve the estimation of precipitation. Using local parameters of variogram has allowed to improve the precipitation predictions as proven by all measures of accuracy (mean absolute error, root-mean-squared error of prediction, and mean relative error) and by the goodness-of-prediction of the estimates obtained by using the global and local parameters of variogram models. These results were also confirmed by R 2 values obtained from two scatterplots of predicted precipitation values using global variogram (R 2 = 0.58) and local parameters (R 2 = 0.74) versus measured values. The two approaches produced a general similar spatial distribution of average annual precipitation, but with specific differences at local level in more orographically complex areas.

Abstract Assessment of drought hazards and evaluation of drought risk are vital for drought risk management and mitigation, as the subject is about providing practitioners and policymakers with useful support. This study aims to evaluate the drought risk using remote sensing data and GIS techniques in Western Iraq based on the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), and precipitation for the years 2013 and 2023. The NDVI was computed based on Landsat images, the SPI was derived from Global Precipitation Measurement (GPM) data, and the precipitation data was collected from GPM, then the three layers was overlaid to produce the risk map. The results showed that, in 2013, NDVI values ranged between 1 to −1, the precipitation values ranged between 6.2 to 26.2 mm, and SPI values ranged between 0.5 to 2.4. While in 2023 the NDVI value ranged between 0.6 to −0.3, the precipitation values ranged between 10.9 to 21.4 mm, the SPI values ranged between 0-1. The drought risk maps were prepared by calculating the class frequency of droughts. The three layers were normalized to a uniform scale and combined using an overlay function to map the drought risk zone. Finally, a resultant risk map was obtained by integrating agriculture and meteorological drought risk maps. The final map classified the study area into three zones: no drought, moderate drought, and high drought. These results may contribute to the establishment of a drought early warning system methodology, considering the influence of urban development and land cover conditions in the analyzed surrounding areas.

Abstract This study applies explainable artificial intelligence (XAI) to analyze the impact of inter‐year variation in weather conditions on yields of oilseed sunflower ( Helianthus annuus L.) across the United States. By integrating historical county‐level yield data from 1976 to 2022 with monthly meteorological data over the same period, we identified key weather predictors influencing sunflower yields at national and state levels along with critical yield‐sensitive threshold temperature and precipitation values that predict reduced yield. Across the sunflower production range, the most critical climate variables identified are July and August maximum temperatures and total precipitation, reflecting yield vulnerability to summer heat waves and drought during budding and flowering. Secondarily, overly cool temperatures during spring planting and establishment (May–June) reduce yields, as do overly cool end‐of‐season temperatures during seed maturation and harvest (September–October), indicating risk of frost or insufficient growing degree days to support plant development. Winter precipitation and temperatures were also detected as important to overall yield dynamics, in particular where wetter winters benefitted yields. Specific temperature and precipitation tipping points vary across the geographic extent of production, but align with existing agronomic knowledge. Our XAI approach enhances model transparency, offering valuable insights for farmers and policymakers to develop adaptive strategies for sunflower cultivation under climate change. Future research incorporating additional factors like soil characteristics and agricultural practices can further refine yield predictions.

This study evaluates the retrofit design of the Semat weir on the Kali Gawe in Jepara Regency. The retrofit aims to adjust the weir’s hydraulic capacity to accommodate estimated flood discharges while ensuring the structure’s stability under applied loads. In the agricultural context, adequate water availability for irrigation directly affects crop yields; conversely, the rainy season often increases river flow and flood risk. Irrigation structures such as weirs are therefore required to raise river water levels to divert flow into irrigation channels and to regulate water distribution. Flood discharge estimates were derived from precipitation data and watershed (drainage basin) characteristics. Flood hydrograph planning is a critical design step for the weir. Log-Pearson Type III analysis was used to determine probable precipitation values for several recurrence intervals. Those design precipitation values were then converted into design flood discharges using synthetic unit hydrograph methods, specifically the Snyder, Nakayasu, and Gamma HSS approaches. Employing the Gamma synthetic unit hydrograph for the 50-year return period (Q50) produced a design flood discharge of 2,536.52 m³/s for that recurrence interval. Structural stability analyses of the redesigned weir indicate safety factors well above customary thresholds: overturning resistance factor = 11.6 (required ≥ 1.5), sliding resistance factor = 4.80 (required ≥ 2.0), and piping (internal erosion) factor = infinite (required ≥ 4). All evaluated stability parameters therefore satisfy standard safety criteria.

The paper considers a relevant problem of short-term forecasting of daily precipitation using meteorological information and data obtained from measurements of the electric field strength of the atmosphere, but without involving data on past precipitation values. The studies showed high efficiency of applying artificial intelligence in solving the problem, in particular, machine learning methods such as gradient boosting models, decision trees, and neural networks. The data for the study over the period from 2020 to 2025 were obtained from the Nalchik weather station (Russia, WMO ID 37212) and the EFM-100 electric field strength meter installed on the roof of the High-Mountain Geophysical Institute building.

Toward ecological forecasting of West Nile virus in Florida: Insights from two decades of sentinel chicken surveillance.

Abstract This study evaluates the precipitation retrieval performance of 13 passive microwave (PMW) radiometers used as input to the Japan Aerospace Exploration Agency (JAXA) Global Satellite Mapping of Precipitation (GSMaP) dataset, relative to the Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) observations. These PMW sensors include the GPM Microwave Imager (GMI), Advanced Microwave Scanning Radiometer 2 (AMSR2), four Special Sensor Microwave Imager/Sounders (SSMISs), two Advanced Technology Microwave Sounders (ATMSs) on board the Suomi National Polar-Orbiting Partnership (SNPP) and the NOAA-20 satellites, and five Microwave Humidity Sounders (MHSs) on board NOAA-18, NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. We identified several key factors affecting the performance of these 13 sensors, including channel availability, retrieval algorithm, spatial resolution, and snowfall retrieval scheme. Over ocean, AMSR2 and GMI show the best performance, mainly due to their finer spatial resolution and the usage of ∼10 GHz. Interestingly, GMI and three SSMISs (on board F16, F17, and F19) exhibit several identical precipitation values. Further analyses indicate that these artificial values appear under snowfall conditions, suggesting an issue with the snowfall algorithm. Over land, the precipitation intensity retrieved from GMI, AMSR2, and SSMISs shows high concentrations near specific values, which also contribute to their poorer performance. Over both ocean and land, MHSs perform better than ATMSs, most likely due to the finer spatial resolution. These results will provide guidance and insights for future PMW algorithm developments and also utilization of the merged satellite precipitation products. Significance Statement The Global Satellite Mapping of Precipitation (GSMaP) is one of the most widely used global multiple-satellite merged precipitation datasets. This study is the first to evaluate the precipitation retrieval performance from 13 passive microwave sensors used as input to GSMaP over both ocean and land. We identify several key factors affecting the performance of these 13 sensors, including channel availability, spatial resolution, snowfall retrieval scheme, and artificial precipitation estimation values.

Stable isotopes of water have many environmental benefits and applications, including water management to determine the origin of water, the age of ground waters, and the main pollution sources. An analysis was conducted on stable isotopes δ18O and δ 2H in rain samples obtained from a single location in Baghdad between 2013 and 2023. A statistical study was employed to determine the factors that affect the spatial and temporal isotopic fluctuations in rainfall. This work aimed to examine the impact of temperature, humidity levels, and precipitation rates on the isotopic composition of precipitation in Baghdad. The results showed that the MWL of Baghdad city, with a d- excess value of 12.69 , was higher than that of the GMWL( δ 2H = 8 δ 18O + 10 ) and less than that of the Mediterranean ( δ 2H = 8 δ 18O + 22 ) . The effect of temperature on precipitation's isotopic composition indicates a positive correlation between temperature and δ18O values (R2= − 0.032), While a negative correlation exists between precipitation and δ18O values (R2= 0.104). It means that the relationship between δ18O and precipitation is not very strong, This could mean that precipitation is not a good indicator of changes in δ8O compared to temperature. The Hybrid single-particle Lagrangian integrated trajectory ( HYSPLIT) backward trajectory model is also used to identify the origins of humidity during precipitation occurrences. Two sources of humidity are the Mediterranean Sea and the Arab Gulf.

The work was carried out on the gridded precipitation dataset of the ARCIS consortium (hereafter ARCIS) that was produced spatializing a daily dataset of 1762 stations coming from 7 regions of Northern Italy and three regions of Central Italy for the period 1961-2023, with a pixel of 4.7 x 4.7 km. The mean yearly precipitation value from 1961 to 2023 shows a large interannual variability. On the other hand, the trend in frequency and intensity of extreme events (daily events above 50 mm) exhibit high space and time variability. Highlighting areas with significant or not significant trends is crucial for designing hydraulic infrastructures and managing the impacts of natural risks such as floods and landslides. Overall, results reveal that 83% and 86% of the areas do not show significant increases respectively in frequency and intensity while 11% and 15% show significant increases and 2% and 3% show significant decreases. Moreover, the spatial pattern of the areas affected by increasing frequency and intensity, highlights the presence of some regions characterized by increasing trend in North Italy at the beta mesoscale (20-200 km) which is the theatre of phenomena like mesocyclones or low-level jets. Finally, it should be noted that the 1991-2023 analysis of seasonal variations highlights a frequency increase of extreme phenomena in autumn (from October to December with a more marked increase in November), a weak decrease from January to April and a marked decrease in August. These variations can be interpreted as the result of the interaction of the synoptic circulation with mesoscale effects triggered by orography and sea surface temperature.

ABSTRACT Focusing on the Kashi River Basin in the Yili region, this study collected stable isotope data from various water bodies between December 2021 and November 2022. Utilizing stable isotope techniques, a modified Stewart model, and MOD16 datasets, we investigated spatiotemporal variations in evaporation. Key findings include: (1) Precise identification of Summer-enriched and Winter-depleted precipitation isotopes, establishing the local meteoric water line that distinctly reflects seasonal characteristics; (2) Revelation of altitudinal variations in river water isotopes with notable temporal fluctuations during Autumn; (3) Demonstration of a robust linkage between evaporation processes and isotopic fractionation in the Kashi River; (4) Clarification of the seasonal pattern of sub-cloud evaporation (stronger in Summer and weaker in Winter) and its significant linear correlation with Δd values (The difference between the stable isotope value of precipitation and the stable isotope value of raindrops at the cloud bottom), providing critical insights for basin-scale hydrological research.

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for understanding drivers of extreme events like the catastrophic floods of 2024. A total of 138 precipitation fields were generated from 670 spatial points. Spatial analysis revealed median precipitation values ranging from 130 to 329 mm/45 days, with the northeast showing the highest accumulations and the southwest showing the driest conditions. Temporal variability was marked by abrupt anomalies, with median peaks up to 462 mm and minima of 33 mm. Significant temporal autocorrelation (lag-1, 45 days) was identified in the central and northern regions, while lag-2 (90 days) showed inverse patterns in the south (correlation coefficient ≈ −0.45). Principal component analysis (KMO = 0.909; Bartlett’s χ2 = 187,990.945; p < 0.05) identified seven dominant modes, with PC1 explaining 26% of total variance and highlighting extremely wet anomalies (e.g., SPI > 2.0). Correlation with the Oceanic Niño Index revealed heterogeneous responses to ENSO phases, with strong El Niño episodes (2009, 2015–2016) associated with precipitation peaks up to 966 mm/45 days. These results underscore the importance of subseasonal scales for understanding climate anomalies and support the development of regional forecast strategies and water management policies under increasing climate variability.

ABSTRACT Precipitation is a highly heterogeneous variable – even on a yearly basis, because its spatial distribution over large areas is affected by topography and nearby large water bodies. This study compares the performance of different kriging variants in order to take into consideration the factors that affect the spatial distribution of yearly precipitation in the country. The results obtained in this work show that kriging with external drift (KEDl) provides the best estimates of precipitation because KEDl is able to interpolate precipitation values that vary from 800 to 3500 mm at locations that are only 110 km apart and because it reflects the effect of topography on precipitation. Finally, the spatial variation of precipitation obtained with KEDl is similar to that obtained with satellite-estimated precipitation, with the advantage that KEDl can be used to generate spatially distributed climatological data with higher resolution and larger temporal extent.

Bone collagen of terrestrial and marine animals (n = 218) recovered from Ottoman period contexts at Aqaba Castle, Jordan (16th–19th centuries CE), were analyzed for δ13C, δ15N, and δ2H isotope ratios. While δ13C and δ15N values showed considerable overlap among species in the hyperarid environment, δ2H values exhibited less overlap, enhancing stable isotopic niche differentiation. In domesticates, δ2H values show trophic enrichments of +18.4‰ from herbivores to omnivores (dogs), and +26‰ to cats which had the highest δ2H values. Fish δ2H values show a positive relationship with increasing trophic level but also moderately correlate with body size (r = 0.61, R2 = 0.37). The offset between collagen δ2H and rainfall (δ2Hmw) values is smaller for camels (−1.4‰), sheep (−4.5‰), and goats (+6.8%), than for chickens (−18.5‰) and cattle (−27.0‰) due to more frequent consumption of 2H-depleted groundwater by the latter species, because of their higher water requirements. Similarities between local precipitation and bone collagen δ2H values for most terrestrial herbivores suggest the utility of δ2H values for geographic provenancing. This is explored by overlapping gazelle and chukar collagen δ2H values over a regional δ2Hmw isoscape, tentatively suggesting these species inhabited the water-stressed highland environments surrounding Aqaba Castle. This study demonstrates the advantages of incorporating bone collagen δ2H values alongside δ13C and δ15N values as a useful environmental proxy, enhancing interpretations of animal dietary behaviour, trophic levels, water sources, and wild animal home ranges.

In this study, the spatial variation of dynamical complexities in selected meteorological variables over Nigeria were investigated. Quantitative tools of nonlinear analysis such as correlation dimension, Lyapunov exponents and approximate entropy were implemented on daily mean values of meteorological variables recorded across Nigeria from 1982-2020. Results obtained from the analysis confirms that there is low-dimensional deterministic chaotic dynamics in the climate across Nigeria as a result of the positive Lyapunov exponent values obtained; with values spatially ranging from 0.00181-0.0591 for precipitation, 0.00696-0.277 for wind speed, 0.00369-0.26 for temperature and 0.00109-0.00698 for all sky radiation. It was observed that the northern part of the country has higher degree of chaos in precipitation and air temperature as the Lyapunov exponents increased latitudinally while the southern part exhibits more chaotic dynamics in wind speed and all-sky radiation as a result of coastal effects. Correlation dimension values for precipitation significantly affirmed nonlinearity with values ranging spatially from 0.106-2.34 (increasing latitudinally from North to South) while wind speed (3.81-4.26), air temperature (3.55-4.25) and all sky radiation (3.59-4.25) also affirmed nonlinearity and low dimensional chaos but showed insignificant spatial trends across the country. Furthermore, precipitation was found to exhibit more steady repetitive pattern in the northern part of the country with entropy values ranging from 0.359-0.837 while wind speed, air temperature and all-sky radiation showed less repetitive patterns along the border locations in the far north and coastal cities in the south with entropy values ranging from 0.703-0.932, 0.724-0.892 and 0.718-0.877 respectively. The irregular trends are attributed to local and external factors such as human anthropogenic activities leading to greenhouse gas emission/global warming, topography of these locations and variations in the position of the Inter-Tropical Discontinuity as a result of different nonlinear resonances emanating from the coupling of the oceans, the atmospheric system and the El Nino/Southern Oscillations (ENSO).

Abstract Forecast-Informed Reservoir Operations (FIRO) seeks to improve the efficiency and effectiveness of reservoir operations by leveraging reliable and skillful meteorological and hydrological forecasts to inform water management decisions. FIRO aims to provide increased operational flexibility to optimize competing resource objectives within a catchment area, its reservoir, and regions downstream. While there are many physical, hydrological, environmental, and engineering considerations that make FIRO potentially viable versus impractical at any given reservoir and dam, the potential success of FIRO nationwide relies fundamentally on quantitative precipitation forecast (QPF) and streamflow forecast skill from numerical weather prediction and/or operational forecast guidance. This study demonstrates that western U.S. watersheds in California, Oregon, and Washington exhibit on average the highest QPF skill based on the critical success index (CSI) for extreme watershed precipitation days (quantified by mean areal precipitation values above the top 1% of wet days locally). Watersheds in New England and the mid-Atlantic exhibit similarly high QPF skill but reveal a more rapid decrease in skill as lead times increase. The national variability in QPF skill is reflected within the U.S. Army Corps of Engineers (USACE) portfolio of dams with highest watershed QPF skill for sites within the Sacramento, Portland, Seattle, San Francisco, Los Angeles, Wilmington, Charleston, Baltimore, and New England Districts, among others. The results of this study motivate further national QPF skill analyses as part of the “FIRO Phase III: National Expansion Pathfinder” effort to develop and apply a FIRO screening process nationally across the USACE portfolio of dams. Significance Statement The viability of Forecast-Informed Reservoir Operations (FIRO) at a given reservoir relies in part on precipitation and streamflow forecast skill. This study illustrates the spatial characteristics of precipitation forecast skill for watersheds across the United States and summarizes the results by the hydrologic region and for U.S. Army Corps of Engineers (USACE) districts in support of FIRO Phase III: the National Expansion Pathfinder effort.

This study analyzed the spatial and temporal variability of precipitation in the western region of São Paulo state, specifically in the Water Resource Management Units of the Aguapeí and Peixe Rivers, using the Rainfall Anomaly Index (IAC) to identify patterns of rainfall heterogeneity at different scales. The mappings were carried out using the free QGIS software, employing the Inverse Distance Weighted (IDW) geospatial interpolation method to estimate precipitation values in areas not directly monitored by rain gauges. The analysis revealed a predominance of dry conditions ("Dry" class) in most of the region, interspersed with areas of above-average rainfall ("Humid" class). Extreme events of both drought and excessive precipitation were identified, particularly in recent decades, highlighting an intensification of climatic variability. The temporal analysis, based on historical series, indicated moderate seasonal trends, with more evident declines during the transition from summer to autumn and relative stability during the critical months of the rainy season. The results highlight the importance of continuous monitoring and the integration of geospatial tools in studying regional climatic dynamics. Moreover, they emphasize the need for adaptive strategies to mitigate the impacts of extreme climatic events on sensitive sectors such as agriculture, water resource management, and urban societies located in vulnerable areas.

Abstract Stable water isotopes have unique physical characteristics and are therefore key in research on paleoclimate proxies and Earth's hydrological cycle. Isotope‐incorporated General Circulation Models (GCMs) are powerful tools that account for the physical processes related to stable water isotopes. In this study, we implemented a global version of the Non‐iteration Dimensional‐split Semi‐Lagrangian (NDSL) advection scheme in the IsoGSM to address a systematic bias in the delta values of surface precipitation over the Antarctic Plateau during austral winter (June–July–August; JJA). Results indicate that the spatial distribution of delta deuterium of surface precipitation during austral summer (December–January–February; DJF) is accurately simulated by both the control experiment (CNTL) and a subsequent NDSL experiment (NDSLQ); however, during austral winter, CNTL and NDSLQ exhibit differences, particularly over East Antarctica. Notably, NDSLQ captures the temperature effect more effectively than CNTL. Analysis of precipitation systems crossing the Korean Peninsula reveals that NDSLQ produces a much more realistic distribution of water vapor than that of CNTL, impacting not only the overall upward motion throughout the troposphere but also the position and intensity of surface precipitation. Overall, we recommend using a global spectral model with the NDSL advection scheme instead of the spectral method to improve the simulation of stable water isotopes over Antarctica and weather systems at mid‐latitudes.