Mitigation Resources Research Articles

Wind energy and insects: reviewing the state of knowledge and identifying potential interactions.

In 2023 the wind industry hit a milestone of one terawatt of installed capacity globally. That amount is expected to double within the next decade as billions of dollars are invested in new wind projects annually. Wildlife mortality is a primary concern regarding the proliferation of wind power, and many studies have investigated bird and bat interactions. Little is known about the interactions between wind turbines and insects, despite these animals composing far more biomass than vertebrates. Turbine placement, coloration, shape, heat output, and lighting may attract insects to turbines. Insects attract insectivorous animals, which may be killed by the turbines. Compiling current knowledge about these interactions and identifying gaps in knowledge is critical as wind power grows rapidly. We reviewed the state of the literature investigating insects and wind energy facilities, and evaluated hypotheses regarding insect attraction to turbines. We found evidence of insect attraction due to turbine location, paint color, shape, and temperature output. We provide empirical data on insect abundance and richness near turbines and introduce a risk assessment tool for comparing wind development with suitable climate for insects of concern. This understudied topic merits further investigation as insects decline globally. Compiling information will provide a resource for mitigation and management strategies, and will inform conservation agencies on what insects may be most vulnerable to the expansion of wind technologies.

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

Physiological and behavioral indicators of water buffaloes with access to heat mitigation resources in a grazing system during the hot season in the subtropics

Water buffaloes are increasingly being raised in similar way to dairy cows, in grazing systems frequently deprived of shade and water for immersion. This study aimed to evaluate the effects of different heat mitigation resources, 1) only shade and 2) shade plus water for immersion, on the behavioral repertoire and physiological characteristics of female grazing buffaloes in the subtropics during the hot season. Twenty buffaloes (Bubalus bubalis), including calves, heifers and non-lactating cows were allocated throughout the hot season (from December 2021 to April 2022) in a grazing system with two different heat mitigation resources (treatments): only shade (SH) and shade plus water for immersion (SHW). Diurnal behavioral repertoire was recorded every 15 days, individually and focally, at every 5 minutes for 12 hours per day. Heart rate (HR; beats/min), respiration rate (RR; breaths/min), rectal temperature (RT; °C) and panting score were also measured. Buffaloes reduced grazing time especially with THI above 75. Buffaloes at SHW spent more time grazing and lying than SH buffaloes (P < 0.05). Cows and heifers had higher ruminating (P < 0.05) and standing time (P < 0.05) compared with calves. Physiological variables did not differ (P > 0.05) between treatments (SH vs. SHW) and were positively correlated with air temperature and THI (P < 0.05). HR was higher for heifers (59.6) and calves (63.5) than for cows (53.3; P < 0.05). RT tended to be higher for heifers (38.4) and calves (38.3) than for cows (38.1; 0.05 < P < 0.10). However, respiration rate did not differ across buffalo categories (P > 0.05). Panting behavior was not reported throughout the observation days (panting score = 0). When both heat mitigation resources, shade and water for immersion were available, buffaloes preferred water for immersion to mitigate the effects of heat stress. Regardless animal category, buffaloes were able to maintain physiological variables within the normal range, indicating that providing shade and/or water for immersion, as environmental enrichment resources for thermoregulation, is suitable for heat stress mitigation in water buffaloes throughout the hot season in the subtropics.

An environmental mitigation behavior model predicting waste reduction among young coastal tourists in Bangladesh

PurposeThe main purpose of this study is to develop an environmental mitigation behavior model (EMBM) for predicting waste reduction behaviors among young coastal tourists in Bangladesh by modifying and extending the theory of planned behavior (TPB).Design/methodology/approachThe research has been administered by applying mixed method (both qualitative and quantitative), where study-1(qualitative) identifies factors, study-2 (quantitative) verifies factors by employing exploratory factor analysis (EFA) approach, and study-3 confirms factors influencing waste reduction behaviors among young coastal tourists by employing confirmatory factor analysis (CFA). Study-2 collects 385 valid responses and analyzes by applying the principal component analysis (PCA) technique with the Varimax rotation method by using SPSS-v25, and Study-3 collects 501 valid responses and analyzes by partial least structural equation modeling (PLS-SEM) technique, using Smart PLS3.3.3.FindingsThe study findings have revealed that waste reduction intention and perceived ecological balance significantly influence waste reduction behaviors among young coastal tourists in Bangladesh. Further, waste reduction intention is significantly predicted by mitigation attitude, resource conservation norms and environmental perceptions. Furthermore, environmental ethics significantly affects mitigation attitudes and resource conservation norms.Practical implicationsThe study offers several insightful implications (e.g. incentives, charging fines, environmental awareness-related knowledge-based campaign, etc.) for policymakers and industry operators, which may be a dynamic cost-effective mechanism for reducing waste at coastal tourism destinations in Bangladesh as well as in the world.Originality/valueThis study addresses the need for developing a model that can explain waste reduction behaviors among young coastal tourists in Bangladesh, and thus, the study uniquely postulates the theory of environmental mitigation behavior for predicting waste reduction behaviors by modifying and extending the TPB.

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Characterization of fuel-induced water contamination: chemical composition, odor threshold, and ecotoxicological implications

Fuel spills pose significant environmental risks, particularly to drinking water sources and aquatic ecosystems. The composition of fuels has changed over the decades to reduce fossil greenhouse gas emissions. In Sweden, although the number of spill incidents has declined, with around 600 cases reported annually, there remains limited knowledge on the environmental and health impacts of modern fuels. This study aimed to address this gap through comprehensive chemical analysis and ecotoxicological assessments of 31 fuel samples, including petrol, diesel, fuel oil, and marine gas oil. Using gas chromatography-mass spectrometry (GC-MS), we determined 53 substances, including aromatic and aliphatic hydrocarbons, ethers, esters, and 17 polycyclic aromatic hydrocarbons (PAHs). A key focus was on forming a stable water-accommodated fraction (WAF) to isolate non-dissolved fuel elements from water, which is crucial for assessing subsurface aquatic life and drinking water production impacts. Results indicated significant differences in fuel odor profiles, with ethers enhancing odor intensity. Petrol components showed higher water solubility than diesel, partly due to ethanol. Ecotoxicological tests revealed varying toxicity across fuels, with petrol showing the highest toxicity to aquatic organisms, although activated sludge exhibited resilience. Fuels containing water-soluble ethers posed the highest risks to drinking water, while modern diesel was of lower concern due to its low solubility and toxicity. In freshwater ecosystems, petrol and hydrophobic toxins in fuel oil had severe effects during spills. Overall, this study offers critical insights into the environmental impact of common fuels, supporting improved risk assessment and management strategies for spill mitigation and water resource protection.

Can we develop a more targeted approach to mitigating seismic risk?

The recent high death tolls caused by large earthquakes are a further indication that earthquakes remain one of the most destructive natural hazards in the world and can seriously threaten the achievement of disaster reduction goals. To effectively reduce the existing seismic risk, the limited available mitigation resources should be allocated to areas with the most severe potential risk. However, identifying localized concentrations of risk requires detailed studies. Here, we propose a strategy to delineate regional high seismic risk zone at a fine resolution and with high confidence. We demonstrate this strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China. Our analyses reveal that the delineated zone accounts for only ~7% of the regional land area but for ~85% of the total financial loss. We recommend prioritizing seismic risk mitigation measures in such high-risk zones, especially for densely populated cities in seismically active areas, to better meet the disaster risk reduction targets in the Sendai Framework.

Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques

Landslides are among the most dangerous hazards in Asia, posing a significant threat to human lives, infrastructure, and sustainable development. Landslide susceptibility maps provide useful insights into hazard potential but lack quantitative exposure assessments to develop targeted mitigation strategies and resource allocation. This study aims to propose an integrated approach for landslide hazards and exposure evaluation using machine learning models on the Google Earth Engine environment and geospatial analysis techniques. A geospatial database was established to predict hazards and evaluate exposure, including data on topography, geology, hydrology, climate features, land use, and building data. The landslide susceptibility map was created with advanced machine-learning algorithms, including Classification And Regression Tree (CART), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The ROC curve and the Wilcoxon signed-rank test were employed to evaluate the performance differences among the CART, GB, RF, and SVM models. The results indicated that the RF model demonstrated the highest performance, leading to its selection for creating a landslide susceptibility map. Landslide exposure was evaluated by overlaying the landslide susceptibility map with building data to quantify the number of affected houses by landslides across districts and communes. The analysis results identified the Cho Don district as the most exposed, with 46,237 households located in high and very high landslide susceptibility zones, followed by Ba Be district (39,631 households), Bac Kan city (37,266 households), Bach Thong district (28,495 households), Cho Moi district (28,436 households), Na Ri district (17,723 households), Ngan Son district (14,142 households), and Pac Nam district (13,034 households). These findings enable the identification of the potential consequences of landslides on infrastructure, human settlements, and livelihoods, contributing to the promotion of disaster reduction and prevention strategies.

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

Landslides pose a noteworthy threat in urban settlements globally, especially in areas experiencing extreme climate and rapid engineering. However, researches focusing on the long-term uninterrupted land use and land cover change (LULCC) impacted on landslide susceptibility mapping (LSM) in rapid urban expansion areas remains limited, let alone different temporal scenarios adjacency thresholds. This work aims to refine the temporal LSM considering spatiotemporal land use and land cover (LULC) and to provide decision makers with governing factors in landslides control during urbanization in mountainous areas. Herein, annual LULC data and landslide inventory spanning from 1992 to 2022 were utilized to map dynamic landslide susceptibility in Wanzhou District of the Three Gorges Reservoir Area, China. Initially, the landslide-related factors were filtered as input features of random forest (RF) model before diagnosis via multicollinearity test and Pearson Correlation Coefficient (PCC). The advanced patch-generating land use simulation (PLUS) model was then invited to fuel temporal susceptibility prediction powered by LULCC projections. Finally, the performance of various scenarios was evaluated using Receiver Characteristic Curve (ROC) curves and Shapley Additive exPlanation (SHAP) technique, with discussions on LULCC temporal adjacency thresholds and mutual feedback mechanism between territorial exploitation and landslide occurrences. The results indicate that the precision of LSM is positively correlated with the time horizon, acted by incorporating the latest LULC and LULCC achieving an area under the curve (AUC) of 0.920. The transition of land from forest to cropland and impervious areas should be avoided to minimize the increase in landslide susceptibility. Moreover, a one-year adjacency threshold of LULCC is recommended for optimal model accuracy in future LSM. This dynamic LSM framework can serve as a reference for decision makers in future landslide susceptibility mitigation and land resources utilization in rapid urban expansion areas worldwide.

Comparisons of Two-Stage Models for Flood Mitigation of Electrical Substations

We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the direct current (DC) and linear programming alternating current (LPAC) power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful, whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an alternating current (AC) power flow model. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods &amp; Analysis. Funding: This work was supported by the Energy Institute, The University of Texas at Austin. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0125 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0125 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Enhancing Disaster Resilience: Insights from the Cianjur Earthquake to Improve Indonesia’s Risk Financing Strategies

Earthquakes pose a growing threat to Indonesia, and the country’s limited resources for risk mitigation propagate its vulnerability. The heavy reliance on ad-hoc government relief for risk transfer is not sustainable. This study aims to provide scientific evidence for formulating disaster risk financing strategies in Indonesia, focusing on active fault areas with high population density. By analyzing people’s responses to risk financing after the November 21, 2022, earthquake in Cianjur Regency, the study examines emotional reactions, risk perceptions, and willingness to participate in risk insurance. The findings highlight three major issues: low participation in insurance programs due to limited insurance literacy and enrollment, the significant financial burden on the government from material losses, and the critical need for disaster insurance schemes for homes and fixed assets. The study emphasizes the urgency of implementing disaster risk financing in Indonesia, with a focus on the vulnerability of poor and low-income households. It also stresses the importance of enhancing disaster risk governance and promoting public-private partnerships on small-scale insurance schemes.

Climatic and anthropogenic impacts on the water balance of Issyk-Kul Lake through its main catchments

Lake Issyk-Kul is an endorheic lake in arid Central Asia that is vital to the region's ecological sustainability and socio-economic development. Climate change and anthropogenic water consumption led to fluctuations in the lake's water level, which affected the water resource. The goal of this study was to examine the impacts of climate change and human activities on the Issyk-Kul water balance by combining the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios with hydrological modeling. The Soil and Water Assessment Tool (SWAT) model was used to incorporate signals of future precipitation and temperature changes. According to the scenarios, the total discharge of the three catchments showed an overall increasing trend with a maximum value of 28.02%. The snow and ice-melt water from March to August was revealed, and the increasing trends only occurred from March to May, with the snow and ice melting peak variations ranging from 0.5% to 2%. The high increase in change appeared in northern catchment of the lake. There was an exceptional upward precipitation trend over the northern catchment, with annual increases ranging from 0.7 to 14.5%, and an average annual temperature of 1.72°C. With slight similarities, the total runoff would increase for all catchments, with an average annual value of 10.6%. The northern catchment was significantly more sensitive to precipitation and warming than the southeastern catchments. Under land use land cover change, average annual discharge decreased with agricultural expansion, with discharge differences ranging from −0.005 to −1.06 m3/s. The findings are useful for decision-makers addressing the challenges of climate change mitigation and local water resource management.

Plants and their roles in combating contemporary global challenges

This paper examined some aspects of contemporary global challenges that have mostly been occasioned by anthropogenic factors which have uncoupled ecological equilibria. These have been made more severe by pressure from the rapidly increasing human population. Some of these global challenges include deforestation, climate change, pollution of air and soils, ground and surface water scarcity/contamination, food shortages, decreased food quality, extreme poverty and decreased overall health and wellbeing. Plants provide a veritable resource for mitigation of these challenges if they are sustainably exploited to provide qualitative food, raw materials, medicine and ecosystem services. The paper also examined the trends and impacts of these global challenges and concluded that it is imperative for research and development efforts to be directed at the restoration of ecosystem balances, where stability has been disrupted, and to maintain such stability in pristine environments.

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Maintenance is becoming an increasingly green process, aiming to simultaneously minimize the impact of assets on the economy, society, and the surrounding environment while also mitigating any adverse consequences arising from maintenance activities. To manage maintenance from a sustainable perspective, appropriate indices able to represent environmental assessment results in an aggregated and synthetic way are necessary, along with rational decision-making tools able to simulate different maintenance alternatives considering various sustainability dimensions. In response to this challenge, a methodology rooted in sustainability indices and RAM analysis is proposed to support decision making with respect to sustainable maintenance practices. By applying the proposed methodology to a proof of concept we demonstrate that the approach enables the evaluation of both the environmental and economic impacts of different maintenance activities. Furthermore, the methodology facilitates the identification of specific environmental dimensions that need improvement, in addition to discerning the distinct contributions of individual components to the environmental and economic impacts at the system level. These insights offer valuable information to support informed decision making regarding the allocation of internal resources for impact mitigation. The obtained results underscore the potential applicability of this approach to industrial systems, signifying its contribution to the goal of achieving sustainable maintenance practices.

Development of Disaster Mitigation and Adaptation Learning Tools With Sets Vision (Science, Environment, Technology, Society) Geography Subject in SMA Negeri Palu

This research aims to develop teaching resources for disaster mitigation and adaptation that are integrated into geography subject with a focus on Science, Environment, Technology, and Society (SETS). Up until stage three define, design, and develop research and development processes are conducted using 4D models. Small-group product trials were conducted as an experimental class. The trial findings suggest that the designed learning gadget is valid, practical, and effective. Expert-validated items have a respectable qualification of 80% for their continued existence. Teachers rated the learning device's practicality at 89.93%, whereas students gave it a rating of 77.97% for SETS learning and 75.50% for courses on disaster mitigation and adaptation. With a score of 0.40, the effectiveness test findings were deemed effective in the medium category. Therefore, the prepared syllabus, lesson plans, modules, and assessments that are the result of learning tools for geography subjects in class XI social studies for Disaster Mitigation material with a SETS vision are appropriate for use as learning tools.

CLIM-SEG: A generalizable segmentation model for heat and flood risk mapping

With the escalating impact of climate change coupled with increased urbanization, many cities will experience extreme heat events and intense flooding. Current modeling approaches often fail to incorporate high-resolution, frequently updated data sources, such as aerial imagery from web mapping platforms, limiting their effectiveness in identifying areas at risk. To address this gap, the paper presents CLIM-SEG, a novel framework for high-resolution urban heat and flood risk assessment, addressing critical gaps in current climate risk modeling. This framework integrates semantic segmentation of aerial imagery with a weighted sum approach that integrates environmental, socioeconomic, and building data to provide comprehensive risk evaluations at the census tract level. CLIM-SEG synthesize land cover data with hazard and vulnerability factors, producing risk scores ranging from 0 to 1. This low-cost and efficient framework can enable urban planners to prioritize resources for flood mitigation and heat adaptation, addressing the limitations of current approaches and contributing to the field of urban planning and climate change adaptation. The propoosed methodology incorporates a custom-curated dataset of 545 aerial images, including 145 manually annotated segmentation maps, to fine-tune advanced semantic segmentation models. The optimized Segmenter model achieves a pixel accuracy of 97.85% and an Intersection over Union (IoU) of 0.9578 for key urban features, significantly outperforming baseline models. Boston is selected to represent an ideal representation for both heat and flood risk, as the city experiences severe urban heat islands, and is susceptible to coastal and riverine flooding, with over 11,000 structures expected to be affected by 2070 due to sea level rise and increased precipitation. Results from flood and heat risk models indicate that census tracts in South End have the highest flood risk, with a weighted score value of 0.825, while census tracts in the Fenway-Kenmore neighborhood show the highest heat risk, with a score of 0.991. Both of these results have also been verified with other heat and flood risk mapping sources for Boston. The proposed framework of CLIM-SEG not only addresses the challenges faced by Boston but also has the potential to be scaled to other urban areas dealing with the impacts of climate change, providing a valuable tool for risk assessment and decision-making in the face of a changing climate.

Spatial patterns of torrential rain in the Haihe River Basin and the corresponding large‐scale circulation

AbstractThe classification of torrential rain is crucial to the flood mitigation and water resources, but the relevant research in the Haihe River Basin (HRB) is lacking. In this study, 156 regional torrential rain events from 1972 to 2022 were classified into two types using cluster analysis: eastern type and southern type. The eastern type is the typical torrential rain in the HRB, accounting for 61.5% of the total days, whereas the southern type mainly occurs in the piedmont plain with larger intensity and scope, which would pose more severe hazards. Corresponding large‐scale circulation patterns of the two types reveal a clear relationship between the HRB torrential rain and ascending motion, associated with the anomalies of low‐level moisture convergence and upper‐level wind divergence. The anomalous upper‐level divergence is accompanied by the northward expansion of South Asian High and intensified westerly jets. In the eastern type, strong low‐pressure anomalies north of Lake Baikal at mid‐level produce anomalous northwesterlies. The cold air from mid–high latitudes encounters enhanced southwesterly moist air accompanied by the northward expansion of Western Pacific Subtropical High (WPSH), resulting in the low‐level moisture convergence. In the southern type, the low‐level anomalous convergence is caused by a relatively weak cyclonic anomaly and significantly strengthened southeasterly moisture from the Pacific. The enhancement of southwesterlies results from the cooperative effect of the remarkably northwesterly WPSH and low‐pressure anomalies in South China Sea. As a whole, the eastern type is the result of convergence of cold air and southwesterly moisture driven by a dipole pattern at mid–high latitudes, whereas the southern type is mainly attributed to the accumulation of abundant southeasterly water vapour. This classification can identify high‐risk areas for torrential rain in the HRB and contribute to the forecast skills of extreme precipitation.

Effects of thermal stress on calf welfare

Cold and heat stress present welfare challenges for dairy calves. The consequences of thermal stress on biological functioning have been well documented, and many housing and management strategies have been evaluated to mitigate those detrimental impacts. In cold weather, mitigation strategies have largely focused on nutritional interventions or limiting heat loss with resources such as bedding or jackets. In hot weather, heat abatement strategies such as supplemental shade, increased environmental air exchange through passive ventilation, and forced air movement through mechanical ventilation have been evaluated. Recently in Wisconsin's continental climate, our group evaluated how 2 aspects of calf welfare—the needs for thermal comfort and social contact (i.e., pair or group housing vs. individual housing)—may align or conflict in winter and summer, respectively. In both seasons, calves pair-housed in outdoor hutches preferred social proximity. When 2 calves shared a hutch, the heat load was greater than for a single calf, which may be beneficial for thermal comfort in winter. In summer, the potential detriments from the additional heat load of 2 calves was mitigated with passive hutch ventilation, which calves preferred. Nonetheless, knowledge gaps remain regarding the impacts of thermal stress on calves' affective states, and much remains unknown about their preferences and motivations for specific thermal stress mitigation resources. Future research to address these gaps could improve our understanding of calf welfare and inform best practices for calf management.

Evaluating Effects of Terraces on Flow Regimes in a Hilly and Mountainous Basin

Terraces play a crucial role in regulating the water cycle. The management and restoration of, and the conservation strategies and plans for basins rely heavily on the assessment of alterations in hydrological characteristics, whether natural or man-made, within these basin systems. This study centers on evaluating the impact of terraces on temporal variations in the hydrological regime within the Laixi River basin, within the context of climate variability. To achieve this, we employed a hydrological model and applied the Indicators of Hydrologic Alteration and Range of Variability Approach (IHA–RVA). The results show that, under the influence of terraces, the annual flow exhibited an overall decreasing trend. There was a noticeable decrease in streamflow from March to July, while from October to December, there was a clear upward trend, with increases ranging from 4.76% to 8.10% at the Guanshenxindi (GSXD) and Watershed outlet (WO) cross-sections. The indicators related to the minimum flow showed an overall increasing trend, whereas all indicators related to the maximum flow exhibited a clear decreasing trend. Both high and low flow pulse counts decreased, while high and low pulse durations increased overall. The overall trend of flow reversal counts also showed a decrease. All hydrological indicators exhibited low levels of alteration, with hydrologic alteration values lower than 33.33%. However, the influence of terraces on the upstream hydrological conditions was more pronounced than their downstream effect. The terraces in the study area have played a beneficial role in the flood regulation, drought mitigation, and water resource conservation of the Laixi River. They have contributed to stabilizing the daily average flow during high flow periods and have had a positive impact on the ecological base flow during low flow periods, thereby improving the aquatic ecological environment. This study provides theoretical support for river regulation within the framework of soil and water conservation measures.