Abstract:
keep_len="250">This article develops a novel decision-oriented framework that strategically deconstructs systems resilience in a way that focuses on systems’ design, capabilities, and management. The framework helps evaluate and compare how system design choices impact system resilience. First, we propose a resilience score based on a piecewise linear approximation to a resilience curve. Using multicriteria decision analysis principles, we score system design alternatives in terms of system-specific capabilities. We estimate the relevance of these capabilities to resilience curve parameters associated with resilience phases. Finally, we interpret the derivatives of resilience with respect to the curve parameter values as the leverage of these parameters. Using multiple levels of weighted sums of the scores, we calculate the first order impact of system design choices first on a proxy for the generic resilience parameters and then on resilience, which allows situational characteristics to be incorporated in their natural terminology while mapping their impact on resilience with a traceable logic. We illustrate the approach by using existing materials to develop an example comparing engineered designs for minimizing post-wildfire flood impacts. This article develops a novel decision-oriented framework that strategically deconstructs systems resilience in a way that focuses on systems’ design, capabilities, and management. The framework helps evaluate and compare how system design choices impact system resilience. First, we propose a resilience score based on a piecewise linear approximation to a resilience curve. Using multicriteria decision analysis principles, we score system design alternatives in terms of system-specific capabilities. We estimate the relevance of these capabilities to resilience curve parameters associated with resilience phases. Finally, we interpret the derivatives of resilience with respect to the curve parameter values as the leverage of these parameters. Using multiple levels of weighted sums of the scores, we calculate the first order impact of system design choices first on a proxy for the generic resilience parameters and then on resilience, which allows situational characteristics to be incorporated in their natural terminology while mapping their impact on resilience with a traceable logic. We illustrate the approach by using existing materials to develop an example comparing engineered designs for minimizing post-wildfire flood impacts.

Abstract:
keep_len="250">This study investigated the crucial role of formulating and applying standard operating procedures (SOPs) in the context of emergency planning and response. The effective application of SOPs during operations is essential in order to address challenges that surpass individual organizational capacities. A specific focus on fostering efficient information sharing and executing specified actions maximizes the prospect of success. To explore the collective functionality of SOPs across organizations, we conducted a comprehensive tabletop exercise involving 10 organizations spanning tactical, operational, and strategic levels. Our analysis identified six key challenges, primarily related to the structures for information sharing, communication pathways, and the complex integration and application of common SOPs within these diverse organizational contexts. This research contributes insights into the use of SOPs in large-scale, cross-organizational scenarios. This study investigated the crucial role of formulating and applying standard operating procedures (SOPs) in the context of emergency planning and response. The effective application of SOPs during operations is essential in order to address challenges that surpass individual organizational capacities. A specific focus on fostering efficient information sharing and executing specified actions maximizes the prospect of success. To explore the collective functionality of SOPs across organizations, we conducted a comprehensive tabletop exercise involving 10 organizations spanning tactical, operational, and strategic levels. Our analysis identified six key challenges, primarily related to the structures for information sharing, communication pathways, and the complex integration and application of common SOPs within these diverse organizational contexts. This research contributes insights into the use of SOPs in large-scale, cross-organizational scenarios.

Abstract:
keep_len="250">The goal of this mixed-methods study was to identify and compare the key capacity considerations regarding public health system resilience before and after the 2023 Türkiye-Syria earthquakes. Public health system resilience was assessed through online and face-to-face workshops using the United Nations Public Health System Resilience Scorecard. The pre-earthquake evaluation was conducted in Istanbul and Trabzon in 2021; the post-earthquake evaluation took place in Hatay and Kahramanmaraş in 2023, with a total of 41 participants each. The online workshops lasted approximately four days, while the face-to-face workshops lasted one day. The study found a significant decrease in the scores for most scorecard resilience indicators in the post-earthquake assessment. Qualitative analysis showed that this decline was largely due to infrastructure collapse. Additionally, defining the roles of health disciplines in disaster management and having data transmission procedures between public health system stakeholders in disasters were among the main capacity considerations in both the pre-and post-earthquake assessments. The post-earthquake evaluation revealed several capacity gaps that had not been addressed in pre-earthquake assessments in areas such as disaster preparedness of vulnerable populations and logistics. The findings highlight the critical importance of strengthening building stock and infrastructure to establish a disaster-resilient public health system. The goal of this mixed-methods study was to identify and compare the key capacity considerations regarding public health system resilience before and after the 2023 Türkiye-Syria earthquakes. Public health system resilience was assessed through online and face-to-face workshops using the United Nations Public Health System Resilience Scorecard. The pre-earthquake evaluation was conducted in Istanbul and Trabzon in 2021; the post-earthquake evaluation took place in Hatay and Kahramanmaraş in 2023, with a total of 41 participants each. The online workshops lasted approximately four days, while the face-to-face workshops lasted one day. The study found a significant decrease in the scores for most scorecard resilience indicators in the post-earthquake assessment. Qualitative analysis showed that this decline was largely due to infrastructure collapse. Additionally, defining the roles of health disciplines in disaster management and having data transmission procedures between public health system stakeholders in disasters were among the main capacity considerations in both the pre-and post-earthquake assessments. The post-earthquake evaluation revealed several capacity gaps that had not been addressed in pre-earthquake assessments in areas such as disaster preparedness of vulnerable populations and logistics. The findings highlight the critical importance of strengthening building stock and infrastructure to establish a disaster-resilient public health system.

Abstract:
keep_len="250">This study aimed to assess sand and dust storm (SDS) risks in arid Central Asia during 2001-2021 from a multisectoral (environment, society, and agriculture) and comprehensive perspective on the Google Earth Engine (GEE) platform. The results show that the areas with moderate or greater SDS risk accounted for 18.75% of the total area of arid Central Asia. The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer. The SDS risk in the oasis area of southern Xinjiang increased significantly, while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years. Khwarazm of Uzbekistan, located in the Amu Darya River Delta, is the administrative district with the highest comprehensive risk of sandstorms, and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms. The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation. This study aimed to assess sand and dust storm (SDS) risks in arid Central Asia during 2001-2021 from a multisectoral (environment, society, and agriculture) and comprehensive perspective on the Google Earth Engine (GEE) platform. The results show that the areas with moderate or greater SDS risk accounted for 18.75% of the total area of arid Central Asia. The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer. The SDS risk in the oasis area of southern Xinjiang increased significantly, while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years. Khwarazm of Uzbekistan, located in the Amu Darya River Delta, is the administrative district with the highest comprehensive risk of sandstorms, and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms. The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.

Abstract:
keep_len="250">Estimating potential casualties from a significant earthquake and tsunami event is crucial to enhance disaster preparedness and response. Although various approaches exist to assess potential casualties, few studies have made direct comparisons between them. The present study aimed to clarify the differences in the estimation of casualties between an agent-based model (ABM), which can capture detailed evacuation behavior but demands significant computational resources, and a simplified approach at less computational cost by assuming that evacuees would move along a straight line from their initial location to the closest evacuation destination. These different approaches were applied to three coastal cities in Japan—Mihama, Kushimoto, and Shingu in Wakayama Prefecture—revealing significant differences in the estimated results between the ABM and the simplified approach. Notably, when the effects of building collapse due to an earthquake were considered, the mortality rates estimated by the ABM were higher than those estimated by the simplified approach in the three cities. There were also significant differences in the spatial distribution of the estimated mortality rates between the ABM and the simplified approach. The findings suggest that while the simplified approach can yield results more quickly, casualty estimates derived from such models should be interpreted with caution. Estimating potential casualties from a significant earthquake and tsunami event is crucial to enhance disaster preparedness and response. Although various approaches exist to assess potential casualties, few studies have made direct comparisons between them. The present study aimed to clarify the differences in the estimation of casualties between an agent-based model (ABM), which can capture detailed evacuation behavior but demands significant computational resources, and a simplified approach at less computational cost by assuming that evacuees would move along a straight line from their initial location to the closest evacuation destination. These different approaches were applied to three coastal cities in Japan—Mihama, Kushimoto, and Shingu in Wakayama Prefecture—revealing significant differences in the estimated results between the ABM and the simplified approach. Notably, when the effects of building collapse due to an earthquake were considered, the mortality rates estimated by the ABM were higher than those estimated by the simplified approach in the three cities. There were also significant differences in the spatial distribution of the estimated mortality rates between the ABM and the simplified approach. The findings suggest that while the simplified approach can yield results more quickly, casualty estimates derived from such models should be interpreted with caution.

Abstract:
keep_len="250">Floods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe effects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment. Floods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe effects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

Abstract:
keep_len="250">Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction. Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Abstract:
keep_len="250">Flash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds. Flash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Abstract:
keep_len="250">Urban road networks function as surface passage for floodwater transport during extreme storm events to reduce potential risks in the city. However, precise estimation of these flow rates presents a significant challenge. This difficulty primarily stems from the intricate three-dimensional flow fields at road intersections, which the traditional one-dimensional models, such as Storm Water Management Model (SWMM), fail to precisely capture. The two-dimensional and three-dimensional hydraulic models are overly complex and computationally intensive and thus not particularly efficient. This study addresses these issues by integrating a semiempirical flow diversion formula into the SWMM source code. The semiempirical formula, derived from hydraulic experiments and computational fluid dynamics simulations, captures the flow dynamics at T-shaped intersections. The modified SWMM’s performance was evaluated against experimental data, and the original SWMM, the two-dimensional MIKE21, and the three-dimensional FLUENT models. The results indicate that the modified SWMM matches the precision of the two-dimensional MIKE21, while significantly reducing computational time. Compared to MIKE21, this study achieved a Nash-Sutcliffe efficiency of 0.9729 and a root mean square error of 0.042, with computational time reduced by 99%. The modified SWMM is suitable for real-sized urban road networks. It provides a high-precision tool for urban road drainage system computation that is crucial for effective stormwater management. Urban road networks function as surface passage for floodwater transport during extreme storm events to reduce potential risks in the city. However, precise estimation of these flow rates presents a significant challenge. This difficulty primarily stems from the intricate three-dimensional flow fields at road intersections, which the traditional one-dimensional models, such as Storm Water Management Model (SWMM), fail to precisely capture. The two-dimensional and three-dimensional hydraulic models are overly complex and computationally intensive and thus not particularly efficient. This study addresses these issues by integrating a semiempirical flow diversion formula into the SWMM source code. The semiempirical formula, derived from hydraulic experiments and computational fluid dynamics simulations, captures the flow dynamics at T-shaped intersections. The modified SWMM’s performance was evaluated against experimental data, and the original SWMM, the two-dimensional MIKE21, and the three-dimensional FLUENT models. The results indicate that the modified SWMM matches the precision of the two-dimensional MIKE21, while significantly reducing computational time. Compared to MIKE21, this study achieved a Nash-Sutcliffe efficiency of 0.9729 and a root mean square error of 0.042, with computational time reduced by 99%. The modified SWMM is suitable for real-sized urban road networks. It provides a high-precision tool for urban road drainage system computation that is crucial for effective stormwater management.

Abstract:
keep_len="250">Debris flows continuously erode the channel downward and sideways during formation and development, which changes channel topography, enlarges debris flow extent, and increases the potential for downstream damage. Previous studies have focused on debris flow channel bed erosion, with relatively little research on lateral erosion, which greatly limits understanding of flow generation mechanisms and compromises calibration of engineering parameters for prevention and control. Sidewall resistance and sidewall shear stress are key to the study of lateral erosion, and the distribution of the flow field directly reflects sidewall resistance characteristics. Therefore, this study has focused on three aspects: flow field distribution, sidewall resistance, and sidewall shear stress. First, the flow velocity distribution and sidewall resistance were characterized using laboratory debris flow experiments, then a debris flow velocity distribution model was established, and a method for calculating sidewall resistance was developed based on models of flow velocity distribution and rheology. A calculation method for the sidewall shear stress of debris flow was then developed using the quantitative relationship between sidewall shear stress and sidewall resistance. Finally, the experiment was validated and supplemented through numerical simulations, enhancing the reliability and scientific validity of the research results. The study provides a theoretical basis for the calculation of the lateral erosion rate of debris flows. Debris flows continuously erode the channel downward and sideways during formation and development, which changes channel topography, enlarges debris flow extent, and increases the potential for downstream damage. Previous studies have focused on debris flow channel bed erosion, with relatively little research on lateral erosion, which greatly limits understanding of flow generation mechanisms and compromises calibration of engineering parameters for prevention and control. Sidewall resistance and sidewall shear stress are key to the study of lateral erosion, and the distribution of the flow field directly reflects sidewall resistance characteristics. Therefore, this study has focused on three aspects: flow field distribution, sidewall resistance, and sidewall shear stress. First, the flow velocity distribution and sidewall resistance were characterized using laboratory debris flow experiments, then a debris flow velocity distribution model was established, and a method for calculating sidewall resistance was developed based on models of flow velocity distribution and rheology. A calculation method for the sidewall shear stress of debris flow was then developed using the quantitative relationship between sidewall shear stress and sidewall resistance. Finally, the experiment was validated and supplemented through numerical simulations, enhancing the reliability and scientific validity of the research results. The study provides a theoretical basis for the calculation of the lateral erosion rate of debris flows.

Abstract:
keep_len="250">The recent increase of regional wildfire occurrences has been associated with climate change. In this study, we investigated the association between the February to March wildfire points and burned area in the southern region of China (20°N-30°N and 105°E-115°E) and the simultaneous Arctic Oscillation (AO) index during 2001-2022 and 2001-2020, respectively. After removing the El Niño-Southern Oscillation and Indian Ocean Dipole signals, time series of the regional mean fire points and burned area over the study area is significantly correlated with the AO index at -0.37 and -0.47, significant at the 0.1 level. Precipitation significantly affects wildfire variations. The positive AO could trigger a southeastward Rossby wave train and induce anomalous cyclone activity approximately located in the area encompassed by 15°N-27°N and 85°E-100°E. This outcome could help to enhance the southern branch trough and results in positive precipitation anomalies in southern China. This increasing moisture is conductive to reducing wildfire risks, vice versa. Our results are potentially useful for strengthening the understanding of the mechanisms of wildfire occurrences in southern China. The recent increase of regional wildfire occurrences has been associated with climate change. In this study, we investigated the association between the February to March wildfire points and burned area in the southern region of China (20°N-30°N and 105°E-115°E) and the simultaneous Arctic Oscillation (AO) index during 2001-2022 and 2001-2020, respectively. After removing the El Niño-Southern Oscillation and Indian Ocean Dipole signals, time series of the regional mean fire points and burned area over the study area is significantly correlated with the AO index at -0.37 and -0.47, significant at the 0.1 level. Precipitation significantly affects wildfire variations. The positive AO could trigger a southeastward Rossby wave train and induce anomalous cyclone activity approximately located in the area encompassed by 15°N-27°N and 85°E-100°E. This outcome could help to enhance the southern branch trough and results in positive precipitation anomalies in southern China. This increasing moisture is conductive to reducing wildfire risks, vice versa. Our results are potentially useful for strengthening the understanding of the mechanisms of wildfire occurrences in southern China.

Abstract:
keep_len="250">Due to global climate anomalies, the intensity and spatial extent of weather and climate extremes have increased notably. Therefore, extreme events must be studied to ensure agricultural production. In this study, the growing season accumulated temperature above 10 ℃ (GSAT₁₀) was used as the climate regionalization index for maize in the Songliao Plain region, and the study area was divided into three climate zones. The standardized precipitation requirement index (SPRI) and standardized temperature index (STI) were introduced to analyze the spatial and temporal patterns of drought, waterlogging, and heat during the maize growing season from May to September using meteorological station data from the Songliao Plain between 1991 and 2020. The compound event magnitude indices were constructed by modeling the marginal distribution to detect the patterns of compound drought and heat events (CDHEs) and compound waterlogging and heat events (CWHEs), and to assess their potential impacts on maize production. The results show that: (1) The major meteorological disasters in the Songliao Plain region were drought and heat. The areas with prolonged high temperatures were similar to the areas with higher severity of temperature extremes, and were mainly concentrated in the central and southern parts of the study area (Zone 3). (2) The CWHEs mainly occurred in the northern part of the study area (Zones 1 and 2), and the CDHEs predominantly occurred in the central and southern parts of the study area. (3) For most sites on the Songliao Plain, the duration, severity, and intensity of compound extreme events were positively correlated with relative meteorological yield (Y_w). Maize yield reduction was significantly affected by the CDHEs. Due to global climate anomalies, the intensity and spatial extent of weather and climate extremes have increased notably. Therefore, extreme events must be studied to ensure agricultural production. In this study, the growing season accumulated temperature above 10 ℃ (GSAT₁₀) was used as the climate regionalization index for maize in the Songliao Plain region, and the study area was divided into three climate zones. The standardized precipitation requirement index (SPRI) and standardized temperature index (STI) were introduced to analyze the spatial and temporal patterns of drought, waterlogging, and heat during the maize growing season from May to September using meteorological station data from the Songliao Plain between 1991 and 2020. The compound event magnitude indices were constructed by modeling the marginal distribution to detect the patterns of compound drought and heat events (CDHEs) and compound waterlogging and heat events (CWHEs), and to assess their potential impacts on maize production. The results show that: (1) The major meteorological disasters in the Songliao Plain region were drought and heat. The areas with prolonged high temperatures were similar to the areas with higher severity of temperature extremes, and were mainly concentrated in the central and southern parts of the study area (Zone 3). (2) The CWHEs mainly occurred in the northern part of the study area (Zones 1 and 2), and the CDHEs predominantly occurred in the central and southern parts of the study area. (3) For most sites on the Songliao Plain, the duration, severity, and intensity of compound extreme events were positively correlated with relative meteorological yield (Y_w). Maize yield reduction was significantly affected by the CDHEs.