Anomaly Index Research Articles

Flood Risk and Resilience: Evidence from the 2024 Flood in Maiduguri, Nigeria

Flooding poses a critical threat to rapidly urbanizing areas in the Global South, where climate variability and inadequate infrastructure intensify vulnerability. Maiduguri, Nigeria, offers a salient case study, as demonstrated by the 2024 flood, one of the most severe in the city's recorded history. This study triangulates qualitative interviews and quantitative rainfall data (1992–2024) to examine three dimensions of the disaster: (1) repercussions on infrastructure, livelihoods, and marginalized populations; (2) the influence of shifting rainfall patterns on flood severity; and (3) the efficacy of preparedness and response strategies. Results highlight an upward trend in seasonal rainfall (Sen's Slope: 10.27 mm/year) and a Rainfall Anomaly Index of 3.07 2024, confirming the intensification of extreme precipitation events. The flood displaced over 157,000 residents, disproportionately disrupting lives of women, children, and the elderly, highlighting systemic inequalities. Delayed infrastructure maintenance and suboptimal early warning systems were key amplifiers of flood impact. These findings emphasize the need for climate-informed urban planning, reinforced infrastructure resilience, and comprehensive disaster-management protocols. This study enriches scholarly discourse on adapting to and mitigating climate-induced disasters in rapidly expanding urban contexts throughout the Global South.

Assessing consistency in drought risks in India with multiple multivariate meteorological drought indices (MMDI) under climate change.

This study investigates the spatio-temporal consistency of different MMDI formulations and their role in meteorological drought characterization uncertainty under historic and future climates using ERA5 reanalysis, and outputs from eight Coupled Model Intercomparison Project Phase 6 models, respectively, across different climate zones and shared socioeconomic pathways (SSP) in the Indian subcontinent. Six MMDI formulations namely the Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI) are used. A suite of analysis including agreement mapping, category difference analysis and uncertainty contribution analysis using global sensitivity analysis (GSA) are employed to quantify the consistency of MMDIs and uncertainty in drought characterization due to the MMDI formulation. The variation in MMDI consistency due to different reference evapotranspiration (ETo) methods is also studied. Results demonstrate strong agreement among the MMDIs under historic climate. Under climate change scenarios our findings demonstrate broad agreement among majority of the MMDIs across the study domain, but in substantial areas where MMDI not agree, especially for higher emission scenarios and arid zones. Increased uncertainty under climate change is due to SDDI and SPEI projecting dryer conditions while scPDSI projecting wetter conditions in the far future period owing to varying degrees of sensitivity of MMDIs to its constituent variables (Precipitation and ETo). Results also show that the uncertainty due to MMDIs varied considerably based on ETo methods as well. Finally, based on GSA analysis, the most significant sources of uncertainty in drought projections under climate change are attributed to MMDI-GCM interactions and MMDIs for the Penman-Monteith method. Discrepancies in drought estimates caused by the MMDI selection highlight the need for careful evaluation of drought indices before adopting for climate change impact assessment.

Development and evaluation of the modified and standardized rainfall anomaly indices for extreme variability analysis.

This study introduces two refined rainfall anomaly indices-the Modified Rainfall Anomaly Index (MRAI) and the Standardized Rainfall Anomaly Index (SRAI)-to address limitations in the traditional Rainfall Anomaly Index (RAI). The existing RAI struggles to effectively capture extreme wet and dry rainfall conditions and relies on a simplistic formulation. To evaluate these indices on a continental scale, data from the Integrated Multi-Satellite Retrievals for GPM (IMERG) was used for the Conterminous United States (CONUS), enabling scalability to ungaged locations and beyond. Additionally, daily annual maximum series (AMS) from 2,360 NOAA stations provided reference data to enhance the robustness of the analysis. IMERG data informed the MRAI and SRAI, while station data validated the RAI. Findings showed MRAI's kurtosis ranged from 0 to +0.9, consistently higher than SRAI and RAI, while SRAI exhibited a kurtosis range from -0.9 to -0.1. Median index ranges were -2 to 0 for SRAI, 0 to +2 for MRAI, and -1 to +1 for RAI. Comparing MRAI and RAI, performance metrics revealed average PRB of -23.5, RMSE of 0.93, MBR of 0.74, NSE of 0.82, and KGE of 0.53. For SRAI versus RAI, metrics showed PRB of -14.5, RMSE of 1.7, MBR of 0.8, NSE of 0.41, and KGE of 0.46. Spatially, MRAI effectively captured positive anomalies leaning toward wet extremes, while SRAI identified negative anomalies indicating dry extremes. These indices demonstrate significant potential for climate change studies, especially when applied together.

Analysis of spatiotemporal distribution, variability, and trends of rainfall in Wollo area, Northeastern Ethiopia.

Ethiopia's agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen's slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.

A State-Supervised Model and Novel Anomaly Index for Gas Turbines Blade Fault Detection under Multi-Operating Conditions

A State-Supervised Model and Novel Anomaly Index for Gas Turbines Blade Fault Detection under Multi-Operating Conditions

Application of Rainfall Data in Drought Hazard Prediction in The Bengawan Solo River Basin Using The Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI)

This study investigates the impact of the Southern Oscillation Index (SOI) on meteorological droughts in the Bengawan Solo River Basin, Indonesia, from 2004 to 2023. Indonesia’s climate is influenced by the El Niño Southern Oscillation (ENSO), with negative SOI values (El Niño conditions) linked to drought events and positive values associated with the rainy season. This study analyzed the values of multiple stations' Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI). This study evaluates the compatibility of drought indices obtained from the Rainfall Anomaly Index (RAI) and the Standardized Precipitation Index (SPI) with the Southern Oscillation Index (SOI). The study's results indicate that the drought index using the Standardized Precipitation Index (SPI) method yields a higher percentage of compatibility with the Southern Oscillation Index (SOI), averaging 60.78%. The compatibility of the SPI method can also be observed in the number of meteorological dry months during 2010, the year of the most severe La Niña, and in 2015, during the most severe El Niño. Through the SPI method, it can also be demonstrated that rainfall data can accurately reflect drought events and rainy seasons, consistent with climate change occurrences.

Contralateral Coronal-Lambdoid Craniosynostosis Treated with Distraction Osteogenesis and Spring-Mediated Cranioplasty

Introduction: Treatment of multi-sutural craniosynostosis presents unique challenges and requires a patient specific approach. Patients with contralateral multi-suture craniosynostosis are particularly challenging as the surgical technique utilized must induce opposing forces of expansion. When patients present early in life, early dynamic expansion may be preferable to open approaches. Herein we report a 2-stage technique to treat contralateral right unicoronal and left lambdoid synostosis with simultaneous distraction osteogenesis and spring-mediated cranioplasty. Methods: A 3-month-old male presented to our clinic for concern for head asymmetry. A CT scan confirmed right coronal and left lambdoid craniosynostosis. Preoperative and postoperative head CT and 3D photogrammetry were used to compute the Head Shape Anomaly index (HSA) and difference in head volume from a personalized normative reference for shape and volume. Results: The surgery lasted 163 minutes, and the patient received a 168 mL blood transfusion. The patient was discharged after 3 days. Two springs were placed following a 1 cm strip craniectomy of the lambdoid suture, and a 40 mm uniplane cranial distractor was placed after a right fronto-orbital osteotomy. Devices were removed following activation and consolidation phases. There were no major complications. Postoperative 3D head CT showed correction of left lambdoid and right coronal craniosynostosis with improved plagiocephaly. The patient’s postoperative HSA Index improved from 4.39 pre-operatively to 2.85 at 145 days post-operatively. In addition, the difference in intracranial volume from the personalized normative reference improved from -87.35 mL pre-operatively to 1.65 mL post-operatively. Conclusion: This early dynamic expansion technique provided improved safety outcomes including decreased operative time, transfusion requirement, and hospital stay when compared to other techniques for multi-suture correction such as cranial vault reconstruction. This technique also produced desired results in shape correction and correction of volumetric anomalies. This illustrates the importance of innovation to develop patient specific surgical designs that maximize safety and morphologic outcomes.

Modification of IPI Method for Extraction of Short-Term and Imminent OLR Anomalies and Case Study of Two Large Earthquakes

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies.

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

Spatio-temporal characteristics of rainfall and drought conditions are using the different drought indices with geospatial approaches in Karnataka state

Karnataka state drought conditions are assessed via drought indices. Over the past several decades, several indices of drought (DI) have been developed and presented, although some of them are region-specific and have problems regarding their applicability to other climatic circumstances. Additionally, choosing the best DI time step to illustrate the drought condition is difficult because of the DIs' various time steps. The study compares the Standardized Precipitation Index (SPI), Statistical Z-Score, China Z-Index (CZI), Rainfall Anomaly Index (RAI), and Rainfall Departure (RD) to determine which one is most appropriate for the districts of the Karnataka state that are prone to both dry and rainy conditions. This study has pointed out that the best drought indices are SPI and RD, and the least accountable drought indice is China Z index. Droughts were most common in 22 districts in 2009, which represent around 70.97% of the state's landmass. In comparison with the rest of the districts, the Ramanagara district noticed the worst drought conditions in 2003, with rainfall reaching 92.47 mm and SPI -3.68, RAI-6.04, MCZI -2.39, and Z score −2.62. Overall, the results of the study will aid in the organization and improvement of drought, flood, agriculture, and water resource management approaches in the state.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Climatic and hydrological variability in the Oued Lakhdar watershed (Central High Atlas, Morocco)

Abstract The results of this study highlighted significant changes in hydroclimatic conditions over the period studied. Annual rainfall shows significant variability, with years such as 1996 characterised by exceptional humidity, while others, such as 2017, stand out for their particularly marked drought. This variation can be quantified using the Rainfall Anomaly Index (RAI), providing crucial information for water resource management. Temperatures also show seasonal fluctuations, oscillating between hot summers and cold winters. In particular, 2017 was exceptionally warm, while 2011 was unusually cold. In terms of river flows, there has been considerable variability, with some years showing high flows (e.g. 2011) and others with very low flows (e.g. 1990). However, the general trend towards an increase in flow from the 2010 onwards is notable (Sgat station). The RCV index, which classifies years according to surplus or deficit, highlights the predominance of deficit years. In addition, the Monthly Flow Coefficient (MFC) allows us to subdivide the hydrological regime into periods of high and low water. These results highlight the importance of monitoring hydro-climatic conditions for effective management of water resources and the implementation of measures to adapt to climate change.

Estatística descritiva e índice de anomalia de chuva na composição de estudos da precipitação pluviométrica e saneamento ambiental no município de Pindoretama – CE

The distribution of rainfall is an important parameter for determining social welfare, for the development of economic activities and in the planning of infrastructure of a municipality. In addition, the constant study of municipal rainfall is extremely relevant, especially in regions where its distributions are irregular, such as in the Northeast Region of Brazil. Therefore, the present work aimed to carry out a study of the rainfall in the municipality of Pindoretama – CE through descriptive statistics and the Rainfall Anomaly Index (IAC), using the historical series from 1994 to 2023 as a reference. Among the results obtained, it was verified the performance of climatic phenomena for the municipality that interfere in the local precipitation, in addition, the behavior of the monthly rainfall for these years was analyzed, also corroborating the studies so that short, medium and long term data are available. Therefore, through this work, it was verified how relevant the tools of descriptive statistics and IAC are in the studies of rainfall, in the promotion of studies related to municipal environmental sanitation.

Enhancing Drought Resilience through Groundwater Engineering by Utilizing GIS and Remote Sensing in Southern Lebanon

Countries face challenges of excess, scarcity, pollution, and uneven water distribution. This study highlights the benefits of advances in groundwater engineering that improve the understanding of utilizing local geological characteristics due to their crucial role in resisting drought in southern Lebanon. The type of drought in the region was determined using the Standardized Precipitation Index (SPI), Standardized Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Soil Moisture Anomaly Index (SM). The dry aquifer and its characteristics were analyzed using mathematical equations and established hydrogeological principles, including Darcy’s law. Additionally, a morphometric assessment of the Litani River was performed to evaluate its suitability for artificial recharge, where the optimal placement of the water barrier and recharge tunnels was determined using Spearman’s rank correlation coefficient. This analysis involved excluding certain parameters based on the Shapiro–Wilk test for normality. Accordingly, using the Geographic Information System (GIS), we modeled and simulated the potential water table. The results showed the importance and validity of linking groundwater engineering and morphometric characteristics in combating the drought of groundwater layers. The Eocene layer showed a clearer trend for the possibility of being artificially recharged from the Litani River than any other layer. The results showed that the proposed method can enhance artificial recharge, raise the groundwater level to four levels, and transform it into a large, saturated thickness. On the other hand, it was noted that the groundwater levels near the surface will cover most of the area of the studied region and could potentially store more than one billion cubic meters of water, mitigating the effects of climate change for decades.

Detecting 3D Salinity Anomalies from Soil Sampling Points: A Case Study of the Yellow River Delta, China

Rapidly capturing the spatial distribution of soil salinity plays important roles in saline soils’ management. Existing studies mostly focus on the macroscopic distribution of soil-salinity changes, lacking effective methods to detect the structure of micro-regional areas of soil-salinity anomalies. To overcome this problem, this study proposes a 3D Soil-Salinity Anomaly Structure Extraction (3D-SSAS) methodology to discover soil-salinity anomalies and step forward in revealing the irregular 3D structure of soil-anomaly salinity areas from limited sampling points. We first interpolate the sampling points to soil voxels using 3D EBK. A novel concept, the Local Anomaly Index (LAI), is developed to identify the candidate soil-salinity anomalies with the greatest amplitude of change. By performing differential calculations on the LAI sequence to determine the threshold, the anomaly candidates are selected. Finally, we adopt 3D DBSCAN to construct anomalous candidates as a 3D soil-salinity anomaly structure. The experimental results from the Yellow River Delta data set show that 3D-SSAS can effectively identify the 3D structure of salinity-anomaly areas, which are highly correlated with the geographical distribution mechanism of soil salinity. This study provides a novel method for soil science, which is conducive to further research on the complex variation process of soil salinity’s spatial distribution.

Real-Time Prediction of Oil-Type Gas Emissions in Coal-Oil-Gas Coexistence Mines.

Oil-type gas disasters are a frequently occurring concern in coal-oil-gas coexistence mines. In order to actively predict the volume of oil-type gas emissions from floor rocks, this study presents an investigative methodology to forecast the geological conditions of floor rocks in front of the roadway face by utilizing the Direct Current (DC) method. The assessment of electrical resistance in rock formations, which is widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect evaluation of fracture development. As a real-time geological detection index for floor rocks that are 100 m ahead of the roadway face, it enhances predictive capabilities. Moreover, when combined with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to predict the oil-type gas emissions from the floor rocks in the excavation roadway area. The results show consistency with field monitoring outcomes, confirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions in front of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Moreover, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be taken to reduce risks and uphold safety standards.

Construction of a Fine Extraction Process for Seismic Methane Anomalies Based on Remote Sensing: The Case of the 6 February 2023, Türkiye–Syria Earthquake

Identifying seismic CH4 anomalies via remote sensing has been verified as a legitimate method. However, there are still some problems, such as unknown reliability due to the complex characteristics of seismic anomalies. In this study, a multi-dimensional and multi-scale methane seismic anomaly extraction process for remote sensing was constructed with the Robust Satellite Technique (RST) based on the Atmospheric Infrared Sounder (AIRS) CH4 data and then applied to the 2023 Türkiye–Syria earthquake. This study obtained the two-dimensional temporal–spatial distribution of methane anomalies and temporal variation in the anomaly index. Based on this, the three-dimensional profile structure of the 8-day methane anomaly was extracted to determine the reliability of the anomaly. Finally, based on the daily methane anomaly, combined with atmospheric circulation and backward trajectory analysis as auxiliary tools, the influence of air mass migration was excluded to enhance the accuracy of CH4 anomaly determination. The results show that the three-dimensional anomalous structure is consistent with the geological characteristics of tectonic activities, and it appears as a “pyramid” or “inverted pyramid” type in a three-dimensional space. The anomalies caused by air mass migration can be eliminated by combining them with synoptic-scale circulation motion. The time series calculated at the epicenter or a certain point in a region may not accurately reflect the influence of regional or specific tectonic activity in the atmosphere. Thus, the optimal determination of the range and magnitude of atmospheric anomalies caused by tectonic activities is a difficult task for future research.

Precipitation dynamics and its interactions with possible drivers over global highlands

The climate and ecosystem in the highland regions are fragile and vulnerable to climate change. This study used ERA5, CRU, and CHIRPS at 0.5° × 0.5° resolution to assess spatiotemporal precipitation trends over global highlands from 1981 to 2021. The Standardized anomaly index (SAI) is used to evaluate the comparative anomalies between the datasets, while the Mann-Kendall and Sen Slope estimator tests the precipitation trends. Empirical orthogonal function (EOF), pixel-wise correlation, and detrended cross-correlation (DCCA) were employed to investigate the dominant precipitation patterns and their relationships with regional climatic impacts, while the wavelet analysis determines ocean-atmospheric factors in the time-frequency domain. The SAI correlation strongly relates to CRU-CHIRPS for precipitation estimates in North America (NA) R2 = 0.84, Europe R2 = 0.71, and South America (SA), whereas CRU-ERA5 showed better results in the Asian highlands R2 = 0.29, highlighting regional differences in the precipitation dynamics. For precipitation, EOF1 showed a positive variance in most highland regions, but a negative variance prevailed in the Tibetan Plateau (TP), whereas EOF1–2 explained ∼32%, 33%, and 30% variance globally for ERA, CRU, and CHRIPS, respectively. A strong positive correlation is observed between precipitation and mean temperature (TMP) in North America (NA) and the European Alps. However, a significant negative correlation prevailed in the TP and some parts of NA, indicating that these factors can significantly influence the precipitation dynamics in the highlands. Additionally, Detrended Cross-Correlation Analysis (DCCA) has verified that there are positive correlations between precipitation and key climatic factors such as total column water vapor (TCWV), potential evaporation (PEV), and soil moisture (SM). In addition, the influence of ocean-atmospheric (interannual and decadal) coherence patterns originating from the Pacific and Atlantic Oceans indicates a significant impact on precipitation variability, especially in the Asian highlands. This study can contribute to a better understanding of mechanisms and serve as a reference for forecasting precipitation dynamics to develop corresponding strategies in global highlands.

Climate trend analysis in the ramis catchment, upper wabi shebelle basin, Ethiopia, using the CMIP6 dataset

Climate change, a global concern, has significant implications for rainfall and temperature patterns. A critical knowledge gap exists in understanding these implications for the Ramis catchment of the Upper Wabi-Shebelle Basin. This research projects future rainfall and temperature patterns in the Ramis catchment of the Upper Wabe-Shebelle basin. The projection utilized a multimodel comprising eight distinct general circulation models from coupled model intercomparison phase six (CMIP6) data simulation. These models were selected based on their performance, considering two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). The Quantile Mapping (QM) bias correction technique, implemented in R, was used to enhance the reliability of extracted data. The Mann‒Kendall (MK) trend test method was employed to analyze temperature and precipitation trends for two future periods, 2030–2060 and 2061–2090, annually and seasonally. The results suggest a decrease in rainfall during the spring and Winter seasons for 2030 to 2060 and 2061 to 2090 respectively, potentially leading to water scarcity that could impact crop growth and water supply. A decreasing trend in annual rainfall was observed from 2030 to 2060 under SSP2-4.5 and SSP5-8.5 scenarios. The study also uncovered significant and consistent warming trends in maximum and minimum temperatures across the Ramis catchment under both the SSP2-4.5 and SSP5-8.5 scenarios. Rainfall Anomaly Index (RAI) analysis predicts a minor increase in rainfall in the Ramis catchment from 2030 to 2090 under both scenarios, with a notable RAI decrease expected in 2043–2071, indicating potential drought conditions. These findings offer critical insights for regional climate change impact assessments across the Ramis catchment.

Drought Assessment based on Rainfall Anomaly Index (RAI) across Erbil-Iraq

Drought Assessment based on Rainfall Anomaly Index (RAI) across Erbil-Iraq