Climate Data Research Articles

An Experimental Direct Model for the Sky Temperature Evaluation in the Mediterranean Area: A Preliminary Investigation

An Experimental Direct Model for the Sky Temperature Evaluation in the Mediterranean Area: A Preliminary Investigation

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Tree-ring wood density reveals differentiated hydroclimatic interactions in species along a bioclimatic gradient

Climate variations are influencing the growth dynamics of forests, with direct consequences on their biomass productivity. The scientific community, concerned with refining the understanding of tree-climate interactions, has recently used tree-ring density to elucidate ecological mechanisms that may be overlooked by earlywood, latewood, and total tree-ring widths. In this study, we model the sensitivity of intra-annual wood density in tree-rings for 15 species distributed across a broad bioclimatic and biodiverse gradient in the Mexican Republic. Maximum, minimum, latewood, and earlywood density (MaxD, MinD, LWD, and EWD, respectively) were associated with climatic data through correlation analysis and mixed-effects models. Maximum temperature (TMAX) and precipitation (P) stand out as key drivers of temporal density fluctuations, notably influencing MaxD and LWD as indicators of hydroclimatic regime changes. MinD and EWD are less responsive but prove valuable in gauging environmental sensitivity. Abies religiosa from the Southern Semi-Arid Highlands and Picea martinezii from Mediterranean California were the most responsive species to climate variables. These results open new paradigms for densitometry as a proxy for ecological processes that species face in anticipating dieback phenomena, mortality rates, and resilience mechanisms, with implications for productivity and carbon rates in the face of predicted climate variations. However, the wood density-climate relationship is even more complex, and integrative research combining other proxies (e.g., wood anatomy and chemical composition) is recommended to refine wood density variations and understand the causes of the interspecific differences found in this study.

Mapping socio-environmental vulnerability to assess rural migration in Ghana

Rural communities in Ghana, dependent on agriculture and lacking resources and infrastructure, are highly vulnerable to climate and environmental change. Internal migration is often considered as a strategy to mitigate local livelihood constraints. Understanding the challenges of rural communities requires knowledge of local conditions. As only few studies have mapped vulnerable areas in the context of migration in Ghana at a spatially explicit and nationwide level, this study provides a geodata-based examination of how rural areas in Ghana are vulnerable to multiple, co-occurring socio-economic and environmental factors influencing migration. A multifactorial and expert-based weighted overlay analysis was applied, integrating diverse data sources including climate, remote sensing, and recent census data from Ghana. Bivariate maps visualize vulnerable areas where a high impact of the factors coincides with a high rural population density. High levels of factor impact are observed in the northern regions and coastal areas of Ghana. Relatively low impact is found in more central parts of the country. The results align with current net migration rates, confirming the applicability of our method for assessing rural internal migration. This method enhances the understanding of migration dynamics in Ghana and emphasizes the role of spatial data in migration studies.

Impact of Drought in Northeastern Algeria: Comparative Study of the SPI and SPEI Índices

Purpose: The study aimed to evaluate drought trends in Annaba, Algeria, considering global warming and extreme weather, using quantitative indices for improved drought comprehension and prediction. Theoretical Framework:The study assesses changes in drought frequency and severity in the Annaba region of Algeria due to global warming and extreme weather conditions. It employs quantitative indices to enhance drought understanding and prediction. Methodology: The study analyzes temperature and precipitation data (1981-2021) from an Annaba weather station to assess drought characteristics.Standardized indices (SPI, SPEI) and extreme value analysis (Generalized Pareto Distribution) are used to quantify drought intensity, duration, and temperature impacts. Findings: Our analyses reveal a critical divergence in drought assessment tools. While stable monthly temperatures suggest a constant influence on evapotranspiration, the SPEI index (including temperature) indicates increasing drought compared to the precipitation-only SPI. This highlights the importance of considering temperature alongside precipitation for accurate drought assessment. Research Practical and Social Implication: This study emphasizes the significance of considering the interplay among temperature, precipitation, and evapotranspiration for better understanding and predicting drought changes in the Annaba region, Algeria. These insights are crucial for sustainable water resource management and climate change adaptation in the region. Originality/Value:This study distinguishes itself by its comprehensive analysis of long-term climate data to examine drought trends in a specific region (Annaba, Algeria) severely impacted by climate change. By comparing the effectiveness of two drought indices (SPEI and SPI), it offers valuable insights into how to improve drought assessments by considering the role of temperature in evapotranspiration. This research enriches the existing literature on drought management and climate change adaptation, providing a valuable case study for similar arid and semi-arid regions worldwide.

Campus climate impacts on sexual violence: a Bayesian comparison of undergraduate and community colleges

Objective Sexual violence is endemic on college campuses. Four-year campuses present high-risk environments for sexual violence and heavy episodic drinking is a robust risk factor for victimization. However, limited literature exists on sexual violence at two-year institutions, with most research focused on four-year campuses. We examined whether campus climates affect sexual violence prevalence rates. Participants Sexual misconduct campus climate data from two-year and four-year campus students. Methods We used Bayesian logistic regressions to compare sexual victimization odds between two- and four-year campuses. Results Four-year students were twice as likely to have experienced sexual victimization and 2.5 times more likely to engage in heavy episodic drinking compared to two-year students. The risk of sexual victimization associated with heavy episodic drinking was reliably similar across campus types. Conclusions Campus climates reliably impact student’s risk of sexual victimization. Based on these findings, two- and four-year campuses may need to implement distinct prevention services.

Drought deciles index for spatial and temporal assessment of satellite-based precipitation datasets

Obtaining precise and long-term precipitation datasets is vital for addressing hydrological and climatic concerns. Limitation of drought recurrent characterizing and its impact in Iraq have been hampered primarily by unavailability of reliable climate data sets. This work assesses the performance of four gridded precipitation products (GPDs), APHRODITE for the period (1983–2007), CHIRPS for the period (1983–2017), PERSIANN for the period (1983–2017) to evaluate their potential for drought monitoring. This was accomplished by analyzing their ability to estimate the Drought Deciles Index (DDI) at multiple time scales (1-month, 3-month, 6-month, 12-month, and 24-month) across 22 ground stations (GS) in semi-arid Iraq during the period 1970–2018. The study area was divided into three zones based on Köppen's climate classifications. The results confirmed the potential of GPDs for monitoring drought in Iraq. Their performance was stronger for shorter time scales with time scales of 1, 3, and 6 and becomes weaker for time scales of 12 and 24. Furthermore, better performance of GPD found at locations with higher annual precipitation rates and altitudes. Overall, APHRODITE and TRMM showed the greatest potential for accurately representing GS. In addition, it is revealed that the DDI's was capable for effectively analyzing and monitoring drought periods in Iraq.

EcoSonicML: Harnessing Machine Learning for Biodiversity Monitoring in South African Wetlands

Biodiversity monitoring, particularly in a country as diverse as South Africa with its extensive migratory bird population, presents significant challenges. This challenge becomes even more pronounced in environments with a multitude of coexisting bird species, notably wetlands, which serve as crucial breeding and feeding grounds for various avian species. This research will address this challenge by designing a cost-effective sound-based sensor system capable of deployment in diverse wetland ecosystems. The primary aim is to aid in the monitoring of bird species by detecting their presence and distribution and then transmitting this valuable data to a central base station. To assess the system’s feasibility and performance, a series of experiments were conducted at the Rondevlei Nature Reserve in Cape Town, South Africa. These experiments focused on the sensor’s capacity to accurately identify avian species while maintaining robustness in varying environmental conditions. The results yielded promising outcomes, demonstrating the successful identification of bird species. Furthermore, the system exhibited reliability across different weather conditions, positioning it as a viable choice for long-term deployment in wetland environments. Beyond species detection, this project also delved into practical aspects of data transfer and storage efficiency, ensuring the system’s suitability for real-world applications. Modularity was another crucial consideration, simplifying maintenance and upgrades. Moreover, a preliminary cost analysis indicated the cost-effectiveness of the system compared to commercial alternatives. The integration of climate sensors into the monitoring system was explored as a future direction. This addition holds the potential to provide a more comprehensive approach to environmental monitoring by incorporating climate data into the analysis. Such a holistic approach can further enrich our understanding of bird behaviour in relation to changing environmental conditions. The findings of this research have significant implications for avian conservation and ecological studies, particularly in the unique context of South Africa. This project introduces an affordable and practical tool for monitoring bird species in wetland habitats, offering valuable insights into the preservation and management of these critical ecosystems.

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

Comparison of methods to aggregate climate data to predict crop yield: an application to soybean

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

Comparative performance assessment of pilot irrigation schemes in Uganda

Irrigation schemes across sub-Saharan Africa are constructed with the intention of increasing agricultural production to increase food security, reduce poverty and improve economic growth. However, most of these schemes are not performing as expected. This study therefore, diagnosed performance gaps in the pilot irrigation schemes of Mubuku and Doho in Uganda and analysed sustainable improvement options. Data was collected through systematic review of literature and scheme data, direct measurements at the schemes, field surveys, inspections, and key informant interviews. For each scheme, data for climate, irrigation, flow measurements, crop yields and farm gate prices were collected. Comparative indicators of agricultural output, water supply, financial and physical sustainability were used to assess scheme performance using standard approaches by International Water Management Institute (IWMI). The findings showed that the schemes were not performing optimally with crop yields being far below the attainable potential. The major contributing factors to the low performance were low water use efficiency and low agricultural output. Poor flow control, poor water distribution, and poor on-farm water application contributed to low water use efficiency. Low agricultural production was attributed to poor crop yields resulting from poor agronomic practices, poor irrigation scheduling and low produce prices. The financial self-sufficiency indicator pointed to farmers’ inability to operate and maintain irrigation schemes effectively. Improving the irrigation schemes performance requires a multidisciplinary approach targeting the improvement water use efficiency and agricultural output.

Shades of green: Unveiling the impact of municipal green bonds on the environment

Green bonds allocate proceeds towards environmentally beneficial projects and sustainable development goals, distinguishing themselves from traditional bonds primarily in the use of proceeds determination. However, investors often find it challenging to assess the carbon reduction potential of these bonds because of the lack of standardised environmental impact reporting. In response to this, our research constructs a unique set of indicators derived from financial and environmental datasets, using multivariate analysis techniques that can accommodate the detection of both linear and non-linear relationships. A novel method combining kernel Principal Component Analysis (kPCA) and Canonical Correlation Analysis (CCA) is applied to detect spatial–temporal cross-correlation in multivariate datasets. This approach handles variable comparability issues and the differential treatment of categorical and numerical variables. A significant finding of this study emerges when this methodology is applied to financial attributes obtained from green bonds issued by municipal agencies (muni bonds), pollution data and environmental (climate) data from nine California counties.The results of the detailed analysis indicate that there is measurable evidence to indicate relationships between green bond issuance and their use of proceeds for pollution reduction efforts. In particular, the results show a clear and interpretable correlation directly linked to the amount of green bond issuance and the effect this is having on pollution reduction, underscoring the tangible impact these financial instruments have on pollution reduction efforts in California.Conversely, when it comes to detecting spatial–temporal relationships between the use of proceeds from green bond issuance and positive climate change effects, this is inconclusive from the current studies’ analysis. It was found that there were weaker cross-correlation relationships observed between climate and green bond financial data set attributes which is perhaps indicative of the fact that climate change effects take a much longer time frame to occur. As such the findings of the analysis in this regard may not indicate that positive climate change effects are not occurring from green bond initiatives, but rather that the ability to measure detectable improvements to climate with regard to the issuance of green bonds is currently limited and will take longer for such effects to manifest in a statistically detectable fashion from the given data. This is particularly likely to be the case given green bonds are only in their infancy as a financial market, having had the earliest issuance only occurring in the last 15-20 years and only substantial growth in the market over the last 10 years. Therefore, this aspect of the research investigating longer-term climate effects with regard to green bond issuance will take longer to develop.

Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries

AbstractStreamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt as well as groundwater connectivity. In mountainous regions with limestone and dolomite geology, bedrock formations can host karst aquifers, which play a significant role in snowmelt–discharge dynamics. However, mapping complex karst features and the resulting surface‐groundwater exchanges at large scales remains infeasible. In this study, timeseries analysis of continuous discharge and specific conductance measurements were combined with gridded snowmelt predictions to characterize seasonal streamflow response and evaluate dominant watershed controls across 12 monitoring sites in a karstified 554 km2 watershed in northern Utah, USA. Immense surface water hydrologic variability across subcatchments, years and seasons was linked to geologic controls on groundwater dynamics. Unlike many mountain watersheds, the variability between subcatchments could not be well described by typical watershed properties, including elevation or surficial geology. To fill this gap, a conceptual framework was proposed to characterize subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds in terms of conduit flow direction, aquifer storage capacity and connectivity. This framework requires only readily measured surface water and climatic data from nested monitoring sites and was applied to the study watershed to demonstrate its applicability for evaluating dominant controls and climate sensitivity.

Application of GIS in land unit mapping and evaluation of land suitability for agriculture in Uong Bi district – Quang Ninh province

This study aims to apply Geographic Information Systems (GIS) in land unit mapping and the evaluation of land suitability for agriculture in Uong Bi District, Quang Ninh Province, Vietnam. Given the complex and spontaneous changes in land usage driven by market fluctuations and environmental challenges, assessing land quality becomes crucial for sustainable agricultural development. Integrating soil, topographic, and climatic data within a GIS framework led to the creation of land unit maps for 40 different units in Uong Bi district, classified based on soil type, slope, soil depth, soil fertility, and hydrological conditions. The assessment of land suitability for seven main crops, including Litchi, Citrus, Dragon fruit, Cinnamon, Vegetables, Pine, and Macca, was conducted using the FAO framework for land evaluation. The results indicate that Pine and Cinamon have the highest arable area across all crops (with 15,895.40 ha and 15,502.40 ha respectively) while Vegetables show the lowest total arable area (4,063 ha). The findings provide valuable insights into the suitability of land for specific crops, facilitating informed decision-making to optimize land use and improve agricultural production.

Optimization of mine water discharge with the river hydrograph. Case study Samara River in Western Donbas

The paper deals with modeling salt transport in surface watercourses affected by mine water discharge taking into account the river hydrograph, hydrogeological, and climatic data. The developed model enables optimizing the discharge parameters; thus, minimizing the mineralization of river water throughout the year. As the case study, we considered the section of the Samara River in the Western Donbas where mine water is discharged from three storage ponds located in the ravines of Kosminna, Taranova, and Svidovok. Taking into account the pond capacities, it is proposed to discharge at least 60% of mine water from all ponds in December-April, with more than 88% of the most mineralized waters from the pond in the Svidovok ravine. The increase of the river water mineralization at the exit from the coal mining area does not exceed 0.22 g/dm3 on average per year, with a maximum of 0.36 g/dm3 during the low water season. The model novelty is coupling natural and man-made factors forming the water and salt balance in the surface watercourse throughout the year with the optimization methods. The proposed technique can be used to optimize the mode of mine water discharge.

Projected Intensified Hydrological Processes in the Three‐River Headwater Region, Qinghai Tibetan Plateau

AbstractThe Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau.

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State

AbstractUttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs.

Which SDM Model, CLIMEX vs. MaxEnt, Best Forecasts Aeolesthes sarta Distribution at a Global Scale under Climate Change Scenarios?

A precise evaluation of the risk of establishing insect pests is essential for national plant protection organizations. This accuracy is crucial in negotiating international trade agreements for forestry-related commodities, which have the potential to carry pests and lead to unintended introductions in the importing countries. In our study, we employed both mechanistic and correlative niche models to assess and map the global patterns of potential establishment for Aeolesthes sarta under current and future climates. This insect is a significant pest affecting tree species of the genus Populus, Salix, Acer, Malus, Juglans, and other hardwood trees. Notably, it is also categorized as a quarantine pest in countries where it is not currently present. The mechanistic model, CLIMEX, was calibrated using species-specific physiological tolerance thresholds, providing a detailed understanding of the environmental factors influencing the species. In contrast, the correlative model, maximum entropy (MaxEnt), utilized species occurrences and spatial climatic data, offering insights into the species' distribution based on observed data and environmental conditions. The projected potential distribution from CLIMEX and MaxEnt models aligns well with the currently known distribution of A. sarta. CLIMEX predicts a broader global distribution than MaxEnt, indicating that most central and southern hemispheres are suitable for its distribution, excluding the extreme northern hemisphere, central African countries, and the northern part of Australia. Both models accurately predict the known distribution of A. sarta in the Asian continent, and their projections suggest a slight overall increase in the global distribution range of A. sarta with future changes in climate temperature, majorly concentrating in the central and northern hemispheres. Furthermore, the models anticipate suitable conditions in Europe and North America, where A. sarta currently does not occur but where its preferred host species, Populus alba, is present. The main environmental variables associated with the distribution of A. sarta at a global level were the average annual temperature and precipitation rate. The predictive models developed in this study offer insights into the global risk of A. sarta establishment and can be valuable for monitoring potential pest introductions in different countries. Additionally, policymakers and trade negotiators can utilize these models to make science-based decisions regarding pest management and international trade agreements.

Vegetation pattern and topography determine erosion characteristics in a semi‐arid sandstone hillslope‐gully system

AbstractThe hillslope‐gully system serves as the primary contributor to both runoff and sediment yield. The WEPP (Water Erosion Prediction Project) model is often applied to investigate erosion characteristics at hillslope scale, demonstrating a high level of accuracy in simulating water erosion. In this study, according to in situ field monitoring (2014–2020) at a Pisha sandstone hillslope on the Loess Plateau, China, a total of 50 rainfall events’ data were used as climatic data to calibrate the soil parameters, and 11 different vegetation patterns and four slope gradients of hillslope‐gully systems were installed as inputs for the management and slope data, respectively. In systems A, B, C and D, the hillslope gradients were defined as 5°, 8°, 10° and 12° and the gully gradients as 15°, 20°, 25° and 30°, respectively. The results showed that the steeper the slope, the more severe the erosion. However, there was a critical value for the effect of slope on runoff. When the slope exceeded 8° and the gully exceeded 20°, the runoff no longer increased further and even decreased. The reduction in runoff in hillslope‐gully systems was in the following order (in mm): system D (3.4 ± 0.14) > system C (3.4 ± 0.14) > system B (3.39 ± 0.14) > system A (3.12 ± 0.13). Increasing vegetation cover could reduce erosion. Differences in runoff between vegetation patterns were not significant (p > 0.05) and ranged from 8% to 26%. However, there were significant differences in the sediment yield reduction benefits of different vegetation patterns (p < 0.05), ranging from 17% to 66%. It was observed that vegetation located in the lower slope produced a more pronounced effect in mitigating sediment when the degree of cover was the same. We conclude that implementing watershed management strategies based on the vegetation and topographic attributes of hillslope‐gully systems within the Loess Plateau, especially on Pisha sandstone hillslopes, serves as the fundamental approach to achieving sustainable watershed management.