Temperature Data Research Articles

Analysis of Vegetation Coverage Changes and Influencing Factors in Aksu, Xinjiang, China (2000–2020): A Comparative Study of Climate Factors and Urban Development

The ecological environment is fundamental to human survival and development, and China has seen a historical shift from localized to widespread improvements in its ecological conditions. Aksu, a typical ecologically sensitive region in Xinjiang, China, is significant for the study of vegetation dynamics and their driving factors, which is crucial for ecological conservation. This study evaluates the spatiotemporal changes in vegetation coverage in Aksu from 2000 to 2020 using long-term Normalized Difference Vegetation Index (NDVI) data and trend analysis. Additionally, this study explores key factors influencing vegetation changes through correlation analysis with temperature, precipitation, and nighttime light data. The results indicate the following: (1) vegetation coverage in Aksu exhibits significant spatial heterogeneity, with annual NDVI increasing at a rate of 0.83% per year (p < 0.05); (2) the influence of temperature and precipitation on NDVI was weakly correlated from 2000 to 2020; and (3) a strong positive correlation was found between nighttime light intensity and NDVI, suggesting that urban development plays a dominant role in vegetation change, while temperature and precipitation have comparatively minor impacts. The findings provide a scientific basis for ecological conservation and sustainable development in the region.

Thermal Optimalization with CFD Analysis and Real-Time Performance Identification in Briquette Ovens Using Modbus-Based Communication

Briquette ovens that utilize biomass energy frequently face obstacles in achieving consistent heat distribution, resulting in ineffective energy usage and varying product quality. This study tackles these challenges by merging Computational Fluid Dynamics (CFD) analysis, performed with Cradle software, alongside a Modbus-based temperature data acquisition system to significantly boost energy efficiency and temperature regulation. A total of 32 K-type thermocouples were meticulously positioned within the oven to gather real-time temperature data, which was processed through LabVIEW. CFD simulations revealed irregular heat distribution patterns, especially at the rear of the oven. The temperature data obtained via Modbus corroborated these observations, allowing for strategic modifications to enhance heat distribution and minimize energy loss. The system achieved an impressive accuracy rate of 98.89%, with a mere error margin of 1.11%, substantially elevating the oven’s energy efficiency and briquette drying capabilities. This research clearly illustrates that the integration of CFD modeling with real-time data acquisition constitutes a powerful method for optimizing the functionality of industrial heating systems.

Calibrated models for effective clustering: Discriminating operation schedules in occupied buildings

AbstractEuropean directives advocate for end-users to be aware of their energy consumption. However, individual energy monitoring tools, such as energy meters or cost allocators, are not always affordable or technically feasible to install. Therefore, the development of virtual tools that enable the study of energy consumption in existing buildings is necessary. Virtual sensors, particularly based on white-box models, offer the opportunity to recreate these behaviours. When calibrated with measured data, white-box models, which incorporate detailed building physics, become increasingly valuable for designing energy-efficient buildings. This research explores a novel approach to identifying building’s load period directly from energy data generated by these calibrated models. The volume of data generated by white-box models can be overwhelming for visual analysis, but the hypothesis here is that analysing this data through clustering techniques can reveal patterns related to occupant behaviour and operational schedules. By feeding indoor temperature data into the calibrated model and analysing the resulting energy outputs, the research proposes a method to identify the heating, ventilation and air conditioning (HVAC) system operation schedule, free oscillation periods and non-recurrent events. Validation is achieved by comparing the identified periods with actual measured data. This methodology enables the development of a virtual sensor for cost allocation, which minimises the need for physical sensor deployment while complying with European Union directives. The research not only demonstrates high accuracy but also the potential to outperform measured schedule. This suggests the ability of the method to identify missing sensor data or other factors affecting temperature curves, enabling fault detection and diagnostics (FDD). Consequently, this opens doors for setting optimised operation schedules that balance energy efficiency with occupant comfort.

Three-Dimensional Structural Analysis of Sea Temperature During Typhoon Transit

This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer.

Assessing ambient temperature measurements in road cycling races

The impact of heat stress on sports performance is a highly relevant topic. Environmental factors, such as temperature and humidity, play a significant role in determining athletic performance. Cycling computers are commonly used in road cycling to capture various data, including temperature. However, it is crucial to emphasize the importance of accurately measuring ambient temperature. This study aimed to assess the reliability and validity of the Garmin Edge 830 cycling computers in measuring ambient temperature during road cycling competitions. We analyzed temperature data collected by 12 different cycling computers. We focused on assessing these devices’ reliability and validity compared to measurements obtained from an official meteorological station. The interclass correlation coefficient between the devices was 0.468 (95% CI: 0.254 to 0.626; p < 0.001). The temperature readings exhibited a significant variability across the different devices. Furthermore, when comparing the data with the readings from the meteorological station, there was no agreement, as indicated by a Lin's Concordance Coefficient (LCC) of 0.398 and a Mean Absolute Percent Error (MAPE) of 8.87%. This research raises concerns regarding the reliability and validity of cycling computer devices for measuring ambient temperature.

Assessment of Long-Term Trends in Pre-Monsoon Rainfall and Temperature for Various Districts Using Moving Average and Exponential Moving Average Methods

Bangladesh is highly vulnerable to climate change and has faced significant challenges over recent decades, particularly regarding rainfall and temperature variability. These changes have impacted both the environment and the agricultural sector. Many global studies on climate change in Bangladesh utilize climate models to project future scenarios and uncertainties. An analysis of rainfall and temperature data from 1964 to 2016, focusing on the pre-monsoon and monsoon seasons, was conducted using non-parametric methods such as Moving Average and Exponential Moving Average. This study covered the Rajshahi, Rangpur, and Bogra districts, with historical data from the Bangladesh Meteorological Department (BMD). The findings indicate that the average maximum temperature is increasing at 0.0225°C/year in Rajshahi, 0.016°C/year in Rangpur, and 0.0067°C/year in Bogra. Regarding pre-monsoon rainfall, Rajshahi experienced an increase of 0.0311 mm/year, Rangpur 0.0014 mm/year, and Bogra 0.0054 mm/year. Conversely, monsoon rainfall trends showed a decline in Rajshahi by 0.0468 mm/year and in Bogra by 0.0318 mm/year.

Habitat Suitability Modelling for the Red Dwarf Honeybee (Apis florea (Linnaeus)) and Its Distribution Prediction Using Machine Learning and Cloud Computing

Apis florea bees were recently identified in Egypt, marking the second occurrence of this species on the African continent. The objective of this study was to track the distribution of A. florea in Egypt and evaluate its potential for invasive behaviour. Field surveys were conducted over a 2-year period, resulting in the collection of data on the spatial distribution of the red dwarf honeybees. A comprehensive analysis was performed utilizing long-term monthly temperature and rainfall data to generate spatially interpolated climate surfaces with a 1-km resolution. Vegetation variables derived from Terra MODIS were also incorporated. Furthermore, elevation data obtained from the Shuttle Radar Topography Mission were utilized to derive slope, aspect, and hillshade based on the digital elevation model. The collected data were subject to resampling for optimal data smoothing. Subsequently, a random forest model was applied, followed by an accuracy assessment to evaluate the classification output. The results indicated the selection of the mean temperature of coldest quarter (bio11), annual mean temperature (bio01), and minimum temperature of coldest month (bio06) as temperature-derived parameters are the most important parameters. Annual precipitation (bio12) and precipitation of wettest quarter (bio16) as precipitation parameters, and non-tree vegetation parameter as well as the elevation. The calculation of the Habitat Suitability Index revealed that the most suitable areas, covering a total of 200131.9 km2, were predominantly situated in the eastern and northern regions of Egypt, including the Nile Delta characterized by its fertile agricultural lands and the presence of the river Nile. In contrast, the western and southern parts exhibited low habitat suitability due to the absence of significant green vegetation and low relative humidity.

Two-decade variability and trend of chlorophyll-a in the Arabian Sea and Persian Gulf based on reconstructed satellite data

The spatiotemporal variability of chlorophyll-a (Chl-a) in the Arabian Sea (AS) and Persian Gulf (PG) has been widely studied, but long-term trends and influencing factors remain less understood due to data gaps. This study investigates Chl-a variability and trends from 2001 to 2019 using reconstructed MODIS-Terra monthly Chl-a and sea surface temperature (SST) data, employing the Data Interpolating Empirical Orthogonal Functions (DINEOF) method for high-accuracy reconstruction. Results reveal pronounced seasonal variability, with Chl-a peaks exceeding 3 mg m-3 during southwestern monsoons and ranging between 1–3 mg m-3 during northeastern monsoons, with the lowest levels in transitional months. Spatially, the highest Chl-a concentrations were observed in the western and northeastern AS, influenced by summer southwestern (SW) and winter northeastern (NE) monsoons. Trend analysis using Sen’s slope and the Mann-Kendall test indicates significant Chl-a declines (-0.002 to 0) along ASPG coasts, with slight increases (~0.005) in the southeastern AS and southern PG. Rising SST anomalies (SST_A) correlated with reduced Chl-a anomalies (Chl-a_A) in the western AS, while increased wind anomalies (Wind_A) enhanced Chl-a_A in the western AS but decreased it in the southern PG. These findings enhance our understanding of the complex environmental dynamics shaping the ASPG ecosystems.

Glacier velocity and surge detection in the Karakoram region, Pakistan: using remotely sensed data with cross-correlation feature tracking

ABSTRACT This research addresses the knowledge gap regarding glacier surging detection (GSD) and their potential downstream implications for Glacier Lake Outburst Floods (GLOFs) risk. This study investigates GSD across the Karakoram region, Pakistan, focusing on 30 glaciers from 2013 to 2023. The increasing occurrence of irregular and rapid glacial surges poses significant threats to downstream communities, the potential formation of ice dam lakes, and subsequent outburst floods. High-resolution satellite imagery, DEM, temperature, and precipitation data quantified glacier velocity rate (GVR) using feature-tracking, surge detection, and correlations. Findings indicate substantial movement ≥42 m/y during 2015–19 and 2021–22, peaking at 56.98 m/y in 2018–19; with significant Z-Scores, surges were recorded in KG-01, KG-06, KG-07, KG-19, KG-29, and KG-30. The region witnessed significant elevation changes, with a positive change at the terminus of KG-01, KG-06, KG-07, and KG-29 and a negative at the glacier’s accumulation zones. Furthermore, climatic factors, including rising average temperatures (17°C to 20°C) and precipitation (14 to 30 mm/y), significantly influenced glacier velocity, affecting glaciers by 20-43% and 38-59%, respectively. These findings underscore the pivotal role of surge velocities in shaping glacier outlets and influencing lake drainage, potentially leading to GLOFs.

Comparison of water surface temperature retrieval methods from Landsat 9 satellite data

Abstract. Water surface temperature (WST) is a crucial parameter in aquatic systems, influencing both water quality and hydrodynamic conditions of these systems. Landsat 9, the latest satellite in the Landsat program, offers a unique opportunity for retrieving Earth's surface temperature data. Despite numerous efforts to develop methods for retrieving surface temperature from remote sensing data, there have been relatively few studies comparing the performance of these methods. The main objective of this study was to evaluate and compare the effectiveness of various methods for estimating WST in a reservoir on Argentina using thermal images from the new Landsat 9 satellite. WST retrievals were obtained from Landsat Collection 2 (C2), Level 2 products, as well as from the Single-Channel (SC) method and a Split-Window (SW) algorithm applied to Landsat C2, Level 1 images. The comparative analysis showed that, although all three methods performed similarly, the Split-Window method proved to be the most effective for estimating WST in the Río Tercero reservoir (R2 = 0.99, RMSE = 0.77 °C). Finally, this method was used to produce detailed WST distribution maps for the studied reservoir.

The Utilisation of Coconut Shell (Cocos Nucifera) as a Partial Aggregate Replacement on the Properties of Concrete in Terms of Thermal Behaviour

Green environments or environmentally friendly buildings have increasingly captured the attention of researchers. This concept involves the reuse of waste materials to enhance or create new products. Accordingly, this study aims to investigate the utilization of fine coconut shell (FCS) as a partial substitute for sand, focusing on its applications with varying percentages from 10% to 100% on the properties of concrete in terms of thermal conductivity. The initial phase of the research concentrated on characterizing the properties of fine coconut shell and sand using methods such as sieve analysis, laser diffraction sieve technique, specific gravity tests, bulk density measurements, scanning electron microscopy (SEM), and water absorption tests. Subsequently, the mechanical properties of fine coconut shell in concrete, replacing sand partially, were evaluated through slump tests, compressive strength tests, flexural strength tests, modulus of elasticity tests, splitting tensile strength tests, water absorption tests, and water permeability tests. The second phase of the study focused on exploring the low thermal conductivity applications of fine coconut shell concrete, assessed through thermal conductivity tests (k-value) and thermal resistance (r-value) calculations. Upon collecting data, a relationship analysis was conducted to determine the optimal percentage of fine coconut shell replacement. Using this optimal percentage, wall panels were constructed to assess heat penetration into buildings, and the temperature data was validated using Autodesk Ecotect software. The findings indicated that fine coconut shell particles were finer (≤ 600 μm) compared to sand (4.25 mm - 150 μm). In terms of mechanical properties, concrete containing fine coconut shell as a partial replacement for fine aggregate demonstrated superior performance to normal concrete. Moreover, the thermal conductivity values of specimens containing coconut shell were lower than those of normal concrete. In conclusion, the study determined that replacing 50% of fine aggregate with fine coconut shell was optimal, meeting British Standard requirements and aligning with previous research findings.

A long-term dataset of <i>in-situ</i> temperature data reveals wind-induced up- and downwelling in the coastal waters of Nha Trang, South Central Vietnam

In order to implement successful coastal management and protect corals, it is imperative to understand the Nha Trang Bay’s coastal processes and take adequate measures to protect corals and reef structure. This paper aimed to analyze whether sudden variations in physical parameters, such as temperature, could be potentially harmful to coastal coral reefs, in addition to anthropogenic factors such as pollution and intensive fishing. In this paper, the first long-term observation (2008–2019) of temperatures, not only from SST data, but also in situ in coral reefs (10 and 18 m depth) at Nha Trang Bay, South Central Vietnam, was investigated. The data showed that wind-induced upwelling during summer mainly govern the coastal region. In contrast, wind-induced downwelling was found during winter, visible in all three investigated water layers (SST, 10 and 18 m). In winter, the vertical mixing is strong and there is virtually no time-lag between the layers. In summer a scattering layer was formed, the phenomenon where a layer of water with different properties (such as temperature or salinity) is formed, blocking the sinking of water. In summer, correlations with air temperature were not significant, nor were correlations with night cooling, thus having implications for the distribution of nutrients and the health of the coral reefs. However, this was only the situation near the coast. Wavelet analysis shows that the short-term variability is significantly more substantial, caused by the shallow depth of the thermocline, which is much stronger affected by tidal and weather events than in winter. As a result of the combination of large yearly temperature variations (21oC to 31oC) plus increased sediment deposition in the rainy seasons, reefs close to the shore are generally not well-developed. This paper strongly advocates for science-based monitoring of coral reef conditions and underscores the need for law enforcement within the Marine Protected Area of Nha Trang Bay.

Insights into the Relationship between Temperature Variation and NAPL Removal during In Situ Thermal Remediation of Soil in the Presence of NAPL-Water Co-boiling: A Two-Dimensional Visualized Sandbox Study.

Thermal remediation effectively treats sites contaminated with nonaqueous phase liquids (NAPL) by heating soil. A key process is the co-boiling at the water-NAPL interface, which lowers the boiling point due to combined vapor pressures, potentially reducing energy needs. However, determining the optimal end time for heating is challenging due to the invisible nature of underground NAPL, often resulting in excessive energy use. The initial NAPL pool size and distance from the heat source influence the spatiotemporal evolution of the NAPL-water interface, defining three zones: the co-boiling equilibrium zone, a nonequilibrium zone, and an unaffected zone. The temperature data collected by fixed temperature sensors can reflect the spatiotemporal evolution of these zones, offering valuable insights into NAPL removal. This study tackles these challenges using a two-dimensional visualized sandbox integrated with real-time image processing and an array of temperature sensors to monitor the NAPL removal and temperature variation. The results reveal semiquantitatively the impact of different initial NAPL amounts and spatial distributions on temperature variations. An optimized strategy is proposed for temperature sensor positioning, and a qualitative relationship is established between the temperature increase and NAPL removal. These findings can enhance our understanding of subsurface temperature dynamics, supporting more efficient, decarbonized remediation practices.

Climate warming drives population trajectories of freshwater fish

Climate change has emerged as a key threat to biodiversity, leading to broad-scale shifts in distributions of marine and terrestrial species as they attempt to track thermally suitable habitat. By contrast, our understanding of climate responses of freshwater species is relatively undeveloped, limiting our knowledge of whether projected warming will lead to freshwater biodiversity loss. Here, we linked a multicontinental database of riverine fish population abundance time series collected from 1958 to 2019 to temperature data from the same period. Across the sampled localities, waters warmed by 0.21 °C per decade (annual maximum of monthly temperatures). We tested whether fish responded to this change by i) increasing abundance at the cooler poleward limit of species distributions-predicted if warming has opened new opportunities-and ii) decreasing abundance toward the equatorward limit of distributions-predicted if temperatures have exceeded tolerance thresholds. We found that observed population trends were consistent with both of these expected patterns from climatic warming and that the trends were more pronounced in time series covering the longer time periods of 30+ y. The responses consistent with climate change were most evident in species with larger body sizes, higher trophic levels, river-sea migratory behavior, and more widespread distributions. Moreover, positive abundance responses to warming were more likely at higher altitudes where conditions tend to be cooler. These findings indicate that projected future warming will likely lead to widespread shifts in riverine community structure, including abundance declines at the trailing edge of species distributions.

Eulerian Parameter Inference: a probabilistic change of variables for model-based inference with high-variability data sets

Advances in measurement technology have led to the generation of increasingly large data sets across various scientific fields. Data that capture the variability of the underlying system or process, such as single-cell or imaging data, are particularly interesting. However, calibrating computational models to explain this type of data remains challenging.We interpret the model calibration as a Stochastic Inverse Problem (SIP), where the measurements are interpreted as probabilistic samples. Our new SIP solution approach, Eulerian Parameter Inference (EPI), only requires data with large empirical variance and a deterministic simulation model that maps model parameters to simulation results. EPI solves the SIP through a change of variables. This computationally efficient approach allows for direct and point-wise evaluation of parameter densities without ever inverting the simulation model. The resulting parameter distribution captures data variability completely, enabling full data reconstruction. Further, estimating multivariate model parameters can be reduced to a series of one-dimensional problems under the assumption of stochastically independent parameters. This property is vital for complex simulation models and involved problems.With EPI, we explain 1) annual average temperature data of 3168 weather stations around the world with an arithmetic model, 2) German district-specific COVID-19 infection data through a differential equation model, and 3) artificial data generated via a partial differential equation model with independent parameters. With a robust mathematical foundation and an easily accessible Python package, we provide a versatile solution framework for integrating data with a high variance into simulation models that promotes application to various research questions.

The Winter Foehn Footprint Across McMurdo Dry Valleys of Antarctica Using a Satellite‐Derived Data Set‐AntAir v1.0

AbstractContinental‐scale mosaics of satellite‐based surface brightness temperature from thermal infrared band measurements and derived near‐surface air temperatures from geostatistical modeling provide new opportunities for understanding wintertime Foehn wind warming and its potential impacts on the valley floor warming. We have detected and assessed Foehn signatures using a combined data analysis approach from previously developed and validated Antarctic‐wide near‐surface Air temperature data set (AntAir), automatic weather stations from the McMurdo Dry Valleys, and regional climate model simulations at 10 km spatial grid resolution. Self‐organizing maps and data compositing methods on regional climate model outputs provided meteorological context for the AntAir‐derived surface climate information. We conclude that AntAir is suitable for surface climatological analyses and improvements are underway to enhance the spatial resolution to sub‐kilometer grid scales. Finally, by applying a Foehn detection algorithm over 13 years, we present the spatial climatological footprints of Foehn‐induced warming across the Dry Valleys of Antarctica for the first time over the austral winters.

Rural Households' Vulnerability to Climate Variability and Adaptation Strategies in the Case of Begemdir District, Amhara Region, Ethiopia.

Climate change vulnerability is the biggest threat to ecosystems and economies of the world. Hence, this study aims to assess the vulnerability to climate variability adaptation strategies of rural households in Begemdir District, Northwest Ethiopia. In this study, the cross-sectional research design was used to gain a wider and better understanding of vulnerability to climate variability. Both primary and secondary data were used to triangulate the study to maintain validity. A multi-stage random sampling technique was used to select 120 sample households from the study area. Moreover, climatic data, such as rainfall and temperature data were collected from meteorological stations. The data gathered from primary data sources analyzed by using descriptive statistics. Finally, a logistic regression model was employed to identify the factors that affecting households' decisions to climate adaptation strategies in the study area. The results of the study reveal that the overall IPCC-LVI score is 0.49, and the perceived rainfall has decreased over the last decade. This implies livelihoods of the households are vulnerable to climate variability and low adaptive capacity. The age, sex, education level, extension services, land size, credit access, access to climatic information, access to credit, and extension services affected significantly (p < 0.05) households' vulnerability to climate variability in the study area. Thus, the local governments, policymakers, non-governmental organizations, and farming communities need to consider these variables to realize climate change adaptation strategies in the study area. Moreover, higher focus should be given to enhancing education, expanding access to credit, increasing land management support, as well as strengthening extension services to build long-term sustainable climate-resilient practices and mitigate the impacts of climate change vulnerability to households in the study area.

Experimental evaluation of solar collectors

The purpose of the present work was to investigate the association of two important engineering devices, the two-phase thermosyphon and the solar collector. A cycle to obtain high vacuum was set up to manufacture thermosyphons, which were inserted into evacuated glass tubes to compose the structure of a solar collector prototype. The prototype was operated over a period of eight months, during which two other solar collectors were tested simultaneously for comparison purposes. The other two collectors were purchased on the market, one of which already included the thermosyphons, while the other operated without them. Temperature readings were taken at the manifold inlet and outlet, as well as in the main water supply, and in the storage tank. The temperature data were analyzed and could be condensed into just two 24-hour periods, as they showed reproducible behavior. A calorimeter allowed estimating the amount of stored energy, which in this case used twenty 24 hour periods for analysis. It was concluded that the solar collector that uses the thermosyphon can reach higher temperatures at the manifold outlet, but the storage tank can interfere, causing a decrease in the temperature of the water that reaches the consumer.

Spatial Assessment of Soil Erosion and Aridity in Somalia Using the CORINE Model

This study analyzed precipitation patterns, drought conditions, erosivity indices, and arid periods in Somalia using the CORINE model. Utilizing rainfall and temperature data from 50 meteorological stations collected between 2011 and 2019, the study calculated key indices: The Fournier Precipitation Index (35.70%), the Bagnouls-Gaussen Drought Index (61.36), and the erosivity index. The results revealed notable spatial variability in erosivity and aridity across Somalia. Northwestern regions, encompassing 204,978.65 km² (32.14% of the total area), exhibit low erosivity due to reduced rainfall intensity, while southern regions, spanning 252,341.11 km² (39.56%), face high erosivity driven by frequent and intense rainfall events. The study also identified significant arid conditions, with 61.36% of Somalia’s land classified as "dry" and 38.64% as "very dry," highlighting widespread water scarcity and vulnerability. These findings underscore Somalia's environmental challenges, including severe soil degradation, reduced agricultural productivity, and the exacerbation of droughts due to climate change. The integration of the Modified Fournier Index and the Bagnouls-Gaussen Index within the CORINE model provides a comprehensive assessment of the country's susceptibility to erosion and aridity. This research offers critical insights for prioritizing areas that require urgent soil conservation and improved land-use management. It also serves as a vital tool for policymakers and international organizations aiming to mitigate the adverse impacts of climate change, enhance agricultural sustainability, and promote ecological resilience in Somalia’s diverse landscapes.

Evaluation of drought events using multiple drought indices under climate change in the Upper Indus Basin.

Spatiotemporal variations in drought events were examined through multiple drought indices in the Upper Indus Basin (UIB) during the 1980-2020 period using observed climate data of precipitation, temperature (T max., T min., and T mean), and potential evapotranspiration (PET) from 16 meteorological stations. Software like ClimPACT2 (to quality check data and generate SPI, SPEI, and CDD drought indices), DrinC (to generate AI, eRDI and PD drought indices), CMhyd (to bias correct NASA POWER gridded data), and ArcGIS (to map extreme drought years) were used in this study. It was revealed that precipitation decreased at 30mm/decade and maximum and minimum temperatures increased at 0.132°C/decade and 0.23°C/decade, aridity increased by decreasing aridity index at 0.15/decade, and number of consecutive dry days increased at 2days/decade. All drought indices reflected decreasing trends, indicating a warming and drying climatic regime. The eRDI drought index analysis revealed that droughts have occurred for 12.6years in the last four decades, and mild droughts (15%) are more common, followed by moderate droughts (9.4%), severe droughts (4.7%), and extreme droughts (2.4%). The study provides comparisons of drought patterns under changing climate in three distinct climatic and physical regions of UIB, i.e., Jammu, Kashmir, and Ladakh, and reveals that the UIB is not free from droughts.