Reference Period Research Articles

Merging Structural and Reduced-Form Models for Forecasting

Abstract Recent economic crises have posed important challenges for forecasting. Models estimated pre-crisis may perform badly when normal economic relationships have been disrupted. Meanwhile, forecasting, especially in central banks, is increasingly based on a suite of models, following two main approaches: structural (DSGE) and reduced form. The challenge remains to identify which model – or combination of models – is likely to make better forecasts in a changing environment. We explore this issue by assessing the forecasting performance of combinations of a medium-scale DSGE model with standard reduced-form methods applied to the Spanish economy and a reference period that includes both the great recession and the sovereign debt crisis. Our findings suggest that: (i) the mean reverting properties of the DSGE model cause it to underestimate the growth of real variables following the inclusion of crisis episodes in the estimation period; (ii) despite this, reduced-form VARs benefit from the imposition of an economic prior from the structural model; but (iii) pooling information in the form of variables extracted from the structural model with (B)VAR methods does not improve forecast accuracy. By analysing the quantiles of the predictive distributions, we also provide evidence that merging models can help improve the forecast in a context including crisis episodes.

Longitudinal assessment of psychological distress and its determinants in a sample of firefighters based in Montreal, Canada.

Firefighters face elevated risks of common mental health issues, with distress rates estimated at around 30%, surpassing those of many other occupational groups. While exposure to potentially traumatic events (PTEs) is a well-recognized risk factor, existing research acknowledges the need for a broader perspective encompassing multidimensional factors within the realm of occupational stress. Furthermore, this body of evidence heavily relies on cross-sectional studies. This study adopts an intensive longitudinal approach to assess psychological distress and its determinants among firefighters. Participants were recruited from 67 fire stations in Montreal, Canada, meeting specific criteria: full-time employment, smartphone ownership, and recent exposure to at least one PTE, or first responder status. Subjects underwent a telephone interview and were directed to use an app to report depressive, post-traumatic, and generalized anxiety symptoms every 2 weeks, along with work-related stressors, social support, and coping styles. Analyses involved 274 participants, distinguishing between those exceeding clinical thresholds in at least one distress measure (the "distressed" subgroup) and those deemed "resilient." The duration and onset of distress were computed for the distressed group, and linear mixed models were employed to evaluate determinants for each psychological distress variable. Clinical psychological distress was observed in 20.7% of participants, marked by depressive, post-traumatic, and anxiety symptoms, often within the first 4-week reference period. Contextual factors (operational climate, social support, solitude) and individual factors (coping style, solitude and lifetime traumatic events in private life) exhibited more significant impacts on psychological distress than professional pressures within the firefighters' work environment. This study reports lower rates of psychological distress than previous research, possibly attributable to sample differences. It highlights that reported symptoms often represent a combined and transient layer of distress rather than diagnosable mental disorders. Additionally, determinants analysis underscores the importance of interpersonal relationships and coping mechanisms for mental health prevention interventions within this worker group. The findings carry implications for the development of prevention and support programs for firefighters and similar emergency workers.

Exposure to earthquakes and development of ischemic heart disease

BackgroundThe evidence regarding the effect of earthquake exposure on the development of cardiovascular diseases is limited. This study evaluated the association between the 2016 Gyeongju earthquake, which had a magnitude of 5.8, and over 600 subsequent aftershocks occurring within a year in Korea, with the development of ischemic heart disease (IHD) among residents of Gyeongju.MethodsTen years (2010–2019) of medical records from a randomly selected cohort of residents (n = 540,858) in Gyeongju and 3 control cities were acquired from the national health insurance service. Employing difference-in-difference and meta-analyses, the risks of IHD development of Gyeongju residents before (reference: Sep 2014 to Aug 2015; period 1: Sep 2015 to Aug 2016) and after (period 2: Sep 2016 to Aug 2017; period 3: Sep 2017 to Aug 2018; period 4: Sep 2018 to Aug 2019) the earthquake were estimated.ResultsThe monthly average incidence of IHD in Gyeongju was 39.5 persons (per 1,000,000) for reference period and 38.4 persons for period 1. However, the number increased to 58.5 persons in period 2, and 49.8 persons in period 3, following the earthquake. The relative risk (RR) [with a 95% confidence interval] of developing IHD among Gyeongju residents increased by 1.58 times (1.43, 1.73) in period 2, 1.33 times (1.21, 1.46) in period 3, and 1.15 times (1.04, 1.27) in period 4, in comparison to both the control cities and the pre-earthquake reference period. The increase in RR was particularly noticeable among women, adults aged 25–44, and individuals with lower incomes.ConclusionsThe major earthquake in Korea was associated with an increase in the development of IHD among local residents. Individuals exposed to earthquakes may benefit from cardiovascular health surveillance.

Potential impacts of climate change on the sudan-sahel region in West Africa – Insights from Burkina Faso

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031–2060) and far future (2071–2100) compared to the reference period (1985–2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and ERA5 (ECMWF Reanalysis v5)) shows reasonable performance for the main climate variables with some biases. Under the SSP5–8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3 °C by the end of the century. Rainfall amount is projected to increase by 30 % under the SSP5–8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20 % increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1–2.6 and SSP2–4.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region and strengthen population resilience for a sustainable future.

Evaluating the effects of climate change on river water temperature downstream of a hydropower reservoir in western Canada

This paper evaluates the impact of climate change on the water temperature of the Nechako River near the town of Vanderhoof (British Columbia, Canada). To do so, the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic and water temperature model was used with data from 10 climate models representing two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5) over two future time periods (2041-2070 and 2071-2100). The results showed an upward trend in projected water temperatures for all tested discharge rates from the impounding reservoir during the warmest periods of the year. The study found that water temperatures are expected to increase by up to 2.57 °C for the near future (2041-2070) and up to 3.56 °C on average for all flow scenarios studied for a far future (2071-2100) when using SSP5-8.5. The timing of the peak water temperature during the summer is also expected to shift, with maximum water temperatures occurring up to 10 days later than in the reference period. In 10.3% of the far future SSP5-8.5 scenarios, at least one day per summer had a mean daily temperature of at least 24 °C, which exceeds limits of 20 °C for sockeye salmon and 21 °C for white sturgeon which are considered detrimental for the fish. It has been shown that over 50% of sockeye salmon will stop their sustained swimming at water temperatures of 24 °C due to cardiac limitations.

Risk of Stroke and Myocardial Infarction Among Initiators of Triptans

Triptans are contraindicated in patients with ischemic heart disease or previous myocardial infarction, and caution is advised when prescribing these drugs to patients with vascular risk factors. However, controlled observational studies have either shown no association or an apparent lower risk, possibly owing to a channeling of triptans to individuals at low risk of cardiovascular outcomes, and it remains unclear whether avoiding triptan treatment for these patients is meaningful. To establish whether an association between triptans and ischemic events could be demonstrated using a self-controlled design because this type of design is robust to the previously mentioned type of confounding. All people in nationwide Danish registries who were initiating triptans and all the ischemic events that they experienced were identified. A case-crossover design was used to estimate odds ratios (OR) for associations between first-ever triptan use and ischemic outcomes, comparing triptan exposure in the 2-week period up to the event with four 2-week reference periods. Data were obtained for the period January 1995 to August 2022. Included from the population of Denmark were individuals redeeming a prescription for any triptan and experiencing at least 1 of 3 predefined ischemic outcomes. No one was excluded. Initiation of any triptan. Acute myocardial infarction, ischemic stroke, or nonspecified stroke. Identified were a total of 429 612 individuals (median [IQR] age, 38 [28-48] years; 325 687 female [75.8%]) who redeemed a first prescription for a triptan in the study period. Of these patients, 11 (0.003%) had a myocardial infarction with the first triptan prescription in either a focal or referent window (odds ratio [OR], 3.3; 95% CI, 1.0-10.9), 18 (0.004%) had ischemic stroke (OR, 3.2; 95% CI, 1.3-8.1), and 35 (0.008%) had ischemic/nonspecified stroke (OR, 3.0; 95% CI, 1.5-5.9). Case patients had a median age of approximately 60 years and had a high-risk cardiovascular profile. Results of this case-crossover study suggest that triptan initiation was associated with higher risk of ischemic stroke and myocardial infarction. For the individual patient with low background cardiovascular risk, the risk of an ischemic event after triptan initiation was very low.

Women in acute forensic psychiatric care: comparison of clinical, sociodemographic, and detention-related characteristics in pretrial detention, sentence execution, and court-ordered treatment

Compared to men inmates, women display decreased prevalence of severe mental disorder but increased occurrence of substance use disorders (SUD) and higher rates of previous contacts with mental health services. The group of women in detention is highly heterogeneous according to the status of incarceration (pre-trial detention (PTD), sentence execution (SE) and court ordered treatments (COT)). Studies focusing on the comparison of sociodemographic patterns, detention-related and clinical variables between these groups are still lacking. We explored these parameters in 136 women admitted for acute psychiatric care in the sole Geneva forensic unit during a nine year period (2014–2023). Sociodemographic and detention-related data included age, nationality, marital status, presence of children, education attainment, most frequently speaking language, social support, employment before conviction and type of offenses. Clinical variables included the main ICD-10 diagnosis, presence of concomitant SUD, type of personality disorders, presence of suicidal thoughts and attempts at admission, as well as number and mean duration of stays. PTD and SE women had at least 9 years of formal education in 38.9% and 30.3% of cases. Most women in PTD (77.7%), SE (56.6%) and COT (56.2%) groups were Swiss or European citizens. The level of French knowledge was excellent in most of the cases. 43.8% of COT women had at least one child and this percentage is even higher for PTD and SE cases. The employment rate before conviction was also quite high, mainly for PTD and SE (61.1% and 60.6%) and, in a lesser degree, for COT (43.8%) women. Significant social support was present in the vast majority of women without any significant group difference. The distribution of type of offenses did not differ between the three types of detention with a predominance of physical violence, and drug trafficking. The number of stays during the period of reference was significantly higher in COT compared to both SE and PTD women. History of previous inpatient care was also significantly more frequent in COT that SE and PTD women. Adjustment and affective disorders were more often found in SE and PTD cases, these diagnoses were absent in the COT group. In contrast, a main diagnosis of psychotic disorders was found in 62.5% of COT cases compared to only 21.2% in SE and 24.1% in PTD cases. The number of stays, history of inpatient care and diagnosis of psychosis were independent predictors of COT status. In conclusion, the present data reveal the good social integration and emotional support of women needing acute psychiatric care in prison independently of the type of detention. Clinically, women in PTD and SE display more often emotional distress whereas those in COT suffer from acute psychotic symptoms with previous history of psychiatric care and multiple inpatient stays.

Current and Future Patterns of Global Wildfire Based on Deep Neural Networks

AbstractGlobal climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on convolutional neural networks (CNNs) was constructed in the reference period (1997–2014) by using wildfire driving factors and historical burned areas derived from the Global Fire Emissions Database (GFED4s). The shifting spatial patterns of global burned areas in future scenarios for the twenty‐first century was investigated by using shared socioeconomic pathways (SSPs) published by CMIP6. Projected burned areas are analyzed by using nine climate models from CMIP6 under four SSPs (SSP126, SSP245, SSP370 and SSP585) for four defined periods. The evolution of the spatial pattern of global wildfires was further described based on terrestrial ecoregions and GFED regions. The results showed that for the reference period (1997–2014), burned areas were generally distributed in tropical and subtropical regions. The projection results exhibited a systematic increasing trend under the four SSPs from a global perspective in response to climate warming. The increasing trend for the burned area in the SSP370 and SSP585 scenarios was more obvious than that for the SSP126 and SSP245 scenarios. As the severity of the emission scenarios increases, severe wildfires will gradually shift to higher latitudes in the mid‐to‐long term (2061–2080) and long term (2081–2100).

Reliability index of a pipe transporting hydrogen submitted to seismic displacement

The reliability index was determined for a pipe presenting a corrosion defect, subjected to an internal hydrogen pressure, buried in a soil with a reaction and subjected to displacement due to seismic activity. The results are obtained by calculating the failure condition: strain demand greater than strain resistance, which is usually the basis of local strain-based design (LSBD). From a probabilistic point of view, this condition results from the product of the two probability distributions, Demand and Resistance. The Resistance is assumed to follow a normal distribution. The strain Demand is calculated assuming that the earthquake probability density follows a Gutenberg–Richter distribution. The reliability index is calculated by analytical method. This method is used to predict the reliability index of three different reference periods or seismic zones located in France.

Estimating regional house price levels

While quality‐adjusted indices tracing the evolutions of regional house prices are increasingly available, this is less the case for data on regional house price levels. Yet, such information is fundamental to assess housing affordability and barriers to labour mobility. This article puts forward a method to compile regional house price levels that are consistent with the evolutions given by house price indices, representative of the underlying stock of dwellings, and based on the information on house price levels that is available at all dates rather than in a single reference period. Results for Spain show that the decline in house prices following the 2008–2009 crisis initially reduced the dispersion in house prices across Spanish regions, but this dispersion has increased again afterwards. Moreover, the relative housing affordability of Madrid deteriorated compared to all other Spanish regions in the last decade. Monitoring whether COVID‐19 will reverse this trend will be key.

Assessment of the national emission reduction strategies effects for Bulgaria (2020–2029 and after 2030) on surface FPRM and CPRM concentrations

Bulgaria has developed national emission reduction strategies for the period from 2020 to 2029 and the years after 2030, in accordance with EU Directive 2016/2284. Our fundamental aim in this study is to assess the effects of the strategy on the PM near surface concentrations in Bulgaria. All the simulation was done by the modeling system U.S. Environmental Protection Agency (US EPA) Models-3 for 2008 to 2014 period and with 9 km horizontal grid resolution for the selected region – Bulgaria. The meteorological background that was used is with 1°x1° resolution from the National Centers for Environmental Prediction (NCEP) Global Analysis Data. There are 5 emission scenarios structured: 2005 emissions (reference period), 2020–2029 emissions projected with existing measures (WEM) and with additional measures (WAM), projected after 2030 WEM and WAM emissions, as parallel calculations were performed with all of the scenarios. Making parallels between the concentrations, with different scenarios simulated, gives the possibility to evaluate the national emission reduction strategies’ effect.

Does the reference period matter when evaluating the effect of SNAP on food insecurity?

AbstractSupplemental Nutrition Assistance Program (SNAP) is the cornerstone food assistance program in the United States and has been shown to reduce the risk of food insecurity. Most research on the causal effect of SNAP on food insecurity relies on the 12‐month food insecurity scale along with usage of SNAP at any point during the year. However, recent social surveys ask about experiences with food insecurity in the 30 days prior to the survey. In this paper, we examine whether similar protective effects of SNAP against food insecurity are obtained whether using the 30‐day or 12‐month food insecurity scale using the December Supplement of the Current Population Survey for 2002–2019. Results indicate comparable average treatment effects of SNAP in mitigating food insecurity across both 30‐day and 12‐month reference periods.

Food insecurity and water management shocks in Saudi Arabia: Bayesian VAR analysis.

The existing conditions of domestic agricultural production and the resulting products will not be able to fruitfully address the increasing food demand due to the limited fertile land and water resources in Saudi Arabia. Moreover, the escalating threat of a hotter climate, the deterioration in precipitation, and harsh droughts in Saudi Arabia have reduced the predictability of water management efficiency and resulted in the exhaustion of water bodies and serious degradation of ecosystems that have directly affected agricultural systems and indirectly, food security. This study also aims to assess the impact of water efficiency on food insecurity in Saudi Arabia. The study applied the Bayesian Vector Autoregressive (BVAR) model for the reference period for the data extended from 2000-2020. Likewise, we used both impulse response functions (IRFs) and forecasting variance error decomposition (FVED) through 1000 Monte Carlo simulations according to the BVAR model to examine both the response of food insecurity to the shocks on water management efficiency used for various purposes and the decomposition of error variance in food insecurity. The results show that food insecurity was not observed throughout this study. The results of the BVAR analysis indicate that in the short run, the coefficients of water use efficiency are significant based on the Food Insecurity Multidimensional Index (FIMI). Also, the BVAR model provides a better forecast with an interdependence on water use efficiency for agricultural purposes and FIMI. Moreover, the results obtained from IRFs have shown a significant effect of water efficiency on FIMI. Water use efficiency for agriculture and industrial purposes reduces food insecurity while increasing water for services use increases food insecurity. Water use efficiency is the key factor affecting food insecurity in the short run. The results reveal that the water use efficiency shocks will decrease food insecurity. The shocks experienced by food insecurity can be predicted as self-shock over a span of ten years. Emphasis is given to the task of water management that may support food security in Saudi Arabia through implementing and enhancing the water use efficiency as an integral part of achieving the SDGs in Saudi Arabia.

Predicting Operating Income via a Generalized Operating-Leverage Model

We propose a generalized, practitioner-oriented operating-leverage model for predicting operating income using net sales, cost of sales, depreciation, and SG&A. Prior research links operating income directly to these items; hence, our model includes all aggregate revenues and expenses that comprise operating income. Prior research finds that the cost of sales is “much less” sticky than depreciation and SG&A; hence, we use the cost of sales as a proxy for the total variable costs and depreciation and SG&A as proxies for the sticky fixed costs. We introduce a new adjustment to the textbook operating-leverage model so that the ratio of sales to the cost of sales remains constant for the reference and forecast periods. Inspired by prior research, we adjust depreciation and SG&A for cost stickiness. We find that using our generalized operating-leverage model improves the forecast accuracy of next-quarter and next-year operating income predictions compared to predictions made using textbook operating leverage, which is a special case of our model.

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Impacts of the 2021 heat dome on emergency department visits, hospitalizations, and health system operations in three hospitals in Seattle, Washington.

Extreme heat events (EHEs) are associated with excess healthcare utilization but specific impacts on emergency department (ED) operations and throughput are unknown. In 2021, the Pacific Northwest experienced an unprecedented heat dome that resulted in substantial regional morbidity and mortality. The aim of this study was to examine its impact on ED utilization, unplanned hospitalization, and hospital operations in a large academic healthcare system. Retrospective electronic medical records from three Seattle-area hospitals were used to compare healthcare utilization during the EHE compared to a pre-event reference period within the same month. Interrupted time series analysis was used to evaluate the association between EHE exposure and ED visits and hospitalizations. Metrics of ED crowding for the EHE were compared to the reference period using Student's t-tests and chi-squared tests. Additionally, multivariable Poisson regression was used to identify risk factors for heat-related illness and hospital admission. Interrupted time series analysis showed an increase of 21.7 ED visits per day (95% confidence interval [CI]=14.7, 28.6) and 9.9 unplanned hospitalizations per day (95% CI=8.3, 11.5) during the EHE, as compared to the reference period. ED crowding and process measures also displayed significant increases, becoming the most pronounced by day 3 of the EHE; the EHE was associated with delays in ED length of stay of 1.0h (95% CI=0.4, 1.6) compared to the reference period. Higher incidence rate ratios for heat-related illness were observed for patients who were older (incidence rate ratio [IRR]=1.02; 95% CI=1.01,1.03), female (IRR=1.47; 95% CI=1.06, 2.04), or who had pre-existing diabetes (IRR=3.19; 95% CI=1.47, 6.94). The 2021 heat dome was associated with a significant increase in healthcare utilization including ED visits and unplanned hospitalizations. Substantial impacts on ED and hospital throughput were also noted. These findings contribute to the understanding of the role extreme heat events play on impacting patient outcomes and healthcare system function.

Unraveling the Preparatory Processes of the 2023 Mw 7.8–7.6 Kahramanmaraş Earthquake Doublet

Abstract Within a span of 9 hr on 6 February 2023, two significant earthquakes, with magnitudes of Mw 7.8 and 7.6, struck the southeastern part of Türkiye and the northern region of Syria, resulting in significant casualties and widespread economic losses. The occurrence of such intense earthquakes in rapid succession on adjacent faults, especially within a highly complex intraplate region with a multifault network, poses a rare phenomenon, presenting new challenges for seismic hazard analysis in such areas. To investigate whether the preparatory processes for the Mw 7.8–7.6 earthquake doublet could be identified on a large spatial scale prior to the seismic events, we employed a data-driven approach for b-value calculation. The difference in b-values from the background values (Δb) in a reference period were used as inputs, and the cumulative migration pattern (CMP) method, quantitatively describing the migration of seismic activity, was utilized to calculate the corresponding probability distributions. The results indicate a widespread phenomenon of decreasing b-values in the study area over a decade before the occurrence of the earthquake doublet, revealing a significant enhancement of differential crustal stress over a large region. In addition, despite not being the region with the most pronounced decrease in b-values, there is a distinct high probability distribution of CMP near the nucleation points of the earthquake doublet, indicating a spatial and temporal “focus” of increased crustal differential stress in the study area, unveiling the preparatory process of the earthquake doublet. This study reveals quantifiable migration patterns over a long time scale and a large spatial extent, providing new insights into the evolution and occurrence processes of the 2023 Mw 7.8–7.6 Kahramanmaraş earthquake doublet. Moreover, it offers potential clues for seismic hazard analysis in such intraplate regions with multiple fault systems.

DPIM-based global reliability analysis method for the cable-stayed bridges and the determination of global target reliability index

This study presents an approach to analyzing the dynamic global reliability of cable-stayed bridges using the direct probability integral method. The analysis considers the degradation of resistance in the concrete main beam, tower, and cables, as well as the time-varying nature of the load effect. Six types of limit state functions for failure modes are established, including bending failure of the beam, static torsional failure of the beam, wind vibration failure of the beam, transverse buckling failure of the tower, longitudinal strength failure of the tower, and strength failure of the stay cable due to fretting fatigue and corrosion. A time-dependent extreme value mapping is constructed in the time domain, then the joint probability density integral equation of time-varying extreme value mapping of multiple performance functions is established. According to the mean and variance of random variables in the evaluation reference period, the direct probability integral method with Heaviside function (DPIM-H) is employed to solve the equation, and the dynamic global reliability index of cable-stayed bridge is obtained. As an example, the Brotonne cable-stayed bridge’s global reliability is analyzed, and the reasonable value of the global target reliability index is studied considering the structural performance degradation.

Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate.

Global warming and the rising occurrences of climate extremes have become formidable challenges for maize production in northeast China. The optimization of sowing date and variety choice stand out as two economic approaches for maize to enhance its resilience to climate change. Nevertheless, assessment of the potential of optimizing sowing date and variety shift on maize yield at finer scale remains underexamined. This study investigated the implications of optimizing sowing date and implementing variety shift on maize yield from a regional perspective. Compared to the reference period (1986-2005), climate change would decrease by 11.5-34.6% (the range describes the differences among climate scenarios and agro-ecological regions) maize yield in the 2050s (2040-2059) if no adaption measure were to be implemented. The combined adaption (optimizing sowing date and variety shift) can improve maize yield by 38.8 ± 11.3%, 42.7 ± 9.7% and 33.9 ± 7.6% under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. The current sowing window typically falls within the projected optimal sowing window, defined as the period capable of achieving 90% of the maximum yield within the potential sowing window under future climate conditions. Consequently, the potential of the effect of optimizing sowing window on maize yield is limited. In contrast, variety shift results in higher yield improvement, as temperature rise creates favorable conditions for transplanting varieties with an extended growth period, particularly in high latitudes and mountainous regions. Under future climate, cumulative precipitation and compound drought and hot days during maize growing seasons are two key factors influencing maize production. The optimization of sowing date and variety choice can improve maize yield in northeast China. In addition, maize production should consider varieties with longer growth period and drought and heat tolerance to adapt to climate change. © 2023 Society of Chemical Industry.

Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts

Abstract. Global climate models (GCMs) are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing bias-adjusted and downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the Quantile Delta Mapping (QDM) method (Cannon et al., 2015) for bias adjustment. After bias adjustment, we apply a new spatial downscaling method called Quantile-Preserving Localized-Analog Downscaling (QPLAD), which is designed to preserve trends in the distribution tails. Both methods are integrated into a transparent and reproducible software pipeline, which we apply to global, daily GCM surface variable outputs (maximum and minimum temperature and total precipitation) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments (O'Neill et al., 2016) for the historical experiment and four future emissions scenarios ranging from aggressive mitigation to no mitigation, namely SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 (Riahi et al., 2017). We use the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 (Hersbach et al., 2020) temperature and precipitation reanalysis as the reference dataset over the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) reference period of 1995–2014. We produce bias-adjusted and downscaled data over the historical period (1950–2014) and the future emissions pathways (2015–2100) for 25 GCMs in total. The output dataset is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25∘ horizontal-resolution product which is publicly available and hosted on Microsoft AI for Earth's Planetary Computer (https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir/, last access: 23 October 2023).