Extreme Hydrometeorological Events Research Articles

Extreme hydrometeorological events induce abrupt and widespread freshwater temperature changes across the Pacific Northwest of North America

Extreme hydrometeorological events induce abrupt and widespread freshwater temperature changes across the Pacific Northwest of North America

Compound successive dry-hot and wet extremes in China with global warming and urbanization

Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China.

Suspended particulate matter response to extreme forcings in the Bay of Seine

The latest Intergovernmental Panel on Climate Change report of 2023 alerts about an increase in the occurrence and intensity of extreme hydro-meteorological events such as storms and extreme river flows, i.e. drought and floods. Investigating the occurrence of these extreme events in the past 15 years and their impacts on sediment dynamics will provide crucial knowledge for anticipating future trajectories of coastal ecosystems. Time series from in situ observations are analyzed to identify extreme events of river flows and waves and examine their impact on Suspended Particulate Matter (SPM) dynamics in a highly turbid coastal area equipped with a high frequency in situ monitoring station at the interface between the Seine Estuary and the Bay of Seine (northern coast of France). Extreme river flow and wave orbital velocity events are investigated because high river discharge contributes to deliver large amounts of SPM concentration to the bay and strong wave action within the bay can lead to erosion and resuspension of bottom sediments. An original detection method is proposed, based on high frequency in situ observations combined with satellite and model data from 2006 to 2022. Extreme forcings are examined through their specific characteristics (high intensity, long duration, season of occurrence, succession of events), their impact on SPM concentration in the coastal environment and the comparison to mean seasonal dynamics. A positive relationship exists between SPM concentration and high SPM spatial extent and forcing intensity. Extremes are more intense in winter for both forcings and generate larger SPM concentration anomalies. However, extreme events during late spring/summer, periods or mean low forcing intensity, are demonstrated to generate SPM concentration anomalies up to 4 times larger than the monthly mean value, hence possibly strongly impacting the system during these atypical periods. This is particularly important as analyzing the distribution of extreme river flow events over the last 60 years indicated an increase in their occurrence and more important the progressive occurrence of high intensity extreme events during spring/summer periods.

Atmospheric Rivers in South-Central Chile: Zonal and Tilted Events

The extratropical west coast of South America has one of the largest frequencies of landfalling atmospheric rivers (ARs), with dozens of events per season that account for ~50% of the annual precipitation and can produce extreme rainfall events in south-central Chile. Most ARs form an acute angle with the Andes, but, in some cases, the moist stream impinges nearly perpendicular to the mountains, referred to as zonal atmospheric rivers (ZARs). Enhanced surface-based and upper-air measurements in Concepcion (36.8° S), as well as numerical simulations, were used to characterize a ZAR and a meridionally oriented AR in July 2022. They represent extremes of the broad distribution of winter storms in this region and exhibit key features that were found in a composite analysis based on larger samples of ZARs and tilted ARs. The latter is associated with an upper-level trough, broad-scale ascent, extratropical cyclone, and cold front reaching southern Chile. Instead, ZARs are associated with tropospheric-deep, strong zonal flow and a stationary front across the South Pacific, with ascent restricted upstream of the Andes. Consequently, ZARs have minimum precipitation offshore but a marked orographic precipitation enhancement and exhibit relatively warm temperatures, thus resulting in an augmented risk of hydrometeorological extreme events.

Global Evaluation of Simulated High and Low Flows from 23 Macroscale Models

Abstract Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. Significance Statement Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Determining the Environmental Impacts of Extreme Hydrometeorological Events on Fire Management in the Guanacaste Conservation Area, Costa Rica

[Introduction]: The heightened levels of environmental degradation resulting from forest fires and their correlation with climate change are of global concern. In Costa Rica, hydrometeorological events categorized as extreme due to their significant impact, such as Hurricane Otto in 2016 and Tropical Storm Nate in 2017, appear to have induced changes in vegetation and, consequently, forest fuel. [Objective]: Therefore, this study analyzes the impact of extreme hydrometeorological events on the supply of vegetative fuel from 1997 to 2020 in the Platanares, El Avión, and Junquillal sites within the Guanacaste Conservation Area (ACG) in the northwest of the country. [Methodology]: The study utilized data from eight vegetation indices selected for their representation of forest growth. Additionally, field verifications and information collection from official personnel and ACG volunteers were conducted to determine the effect of extreme events on the characteristics of vegetative fuel and its relationship with fire management and climate change adaptation. [Results]: The results obtained suggest that the vegetation in the investigated sites is in a state of natural regeneration but is increasingly exposed to the recurring influence of the ENSO phenomenon in its various phases (Neutral, La Niña, El Niño) and extreme hydrometeorological events, such as Hurricane Otto or Tropical Storm Nate [Conclusions]: These events could be contributing not to the quantity but to the intensity of forest fires.

Changes in the Active Drainage Network and Their Impact on the Hydrological Response and Flood Risk Management Process: A Case Study for a Flysch Mountain Catchment

The active drainage network (ADN), as a dynamic component of a catchment, plays an important role in a catchment’s functioning. Changes in the ADN are the most noticeable during extreme hydro-meteorological events, and they result from, among others, the incorporation of man-origin incisions into the ADN. Knowledge of the parameters of the “real” ADN is a key element in the field of catchment hydrology because the ADN affects the intensity of hydro-, geomorpho-, and biological processes. The goals of this study are to assess (1) the changes in the ADN during extreme hydro-meteorological events (with special attention paid to the human-induced impact on the ADN transformation) and (2) the consequences of the ADN changes on the hydrological response of a catchment and their impact on the flood hazard/risk management processes. The study was performed in a mountain catchment, prone to flash flood occurrences. The ADN was reconstructed with the use of ALS-LiDAR data using GIS tools, and the hydrological response was evaluated by using SCS-CN and GIUH models. The results revealed that the ADN functioning during heavy rainfalls is three to four times denser than the natural-origin river drainage network (RDN) (11.4 km·km−2 vs. 2.9 km·km−2), and the RDN is significantly modified by human-origin elements (e.g., roads, ditches, furrows, etc.—they constitute ca. 1/3 of the ADN). Moreover, significant structural changes in the ADN have occurred, which were confirmed by the Hortonians’ type of analysis. The changes in the ADN have affected the hydrological response of the catchment (predominantly an increase in the peak flow—up to 7%) and the dimensions of the 1% probable flood hazard zone (increase of ca. 5%). It may be concluded that significant changes in the ADN, in the catchment studied, had a moderate impact on the changes in the flood hazard level. The results give a new insight into the flood hazard/risk assessment processes in a small flysch mountain catchment.

Evaluation of the Use of Permeable Interlocking Concrete Pavement in Chile: Urban Infrastructure Solution for Adaptation and Mitigation against Climate Change

There is currently a context of climate change due to the way modern cities are developed, and they are made up mainly of impermeable surfaces and concrete buildings that change the hydrological cycle, causing (i) an increase in temperatures, (ii) the accumulation of stormwater on different surfaces, (iii) overflow in drainage systems, and (iv) the alteration of ventilation patterns, among others. This article presents a case study on the implementation of a permeable interlocking concrete paving (PICP) system, and it develops physical–mathematical modeling using software for the design of a parking lot that currently does not have adequate paving and urban drainage, resulting in sporadic flooding due to heavy rainfall in the city of Temuco, La Araucanía region, Chile. This article’s contribution highlights the application of new technology in Chile, discussing road infrastructure solutions based on sustainable urban drainage systems (SUDSs), which seek to implement feasible alternatives in urban sectors to improve human livelihood. The factors studied include structural and hydrological properties, along with the infiltration analysis of the system according to historical rainfall records in the area. This research concludes that the permeable pavement system with a drainage pipe and smooth roughness coefficient performs satisfactorily for an extreme hydrometeorological event corresponding to 140 mm considering 24 h of rainfall with a return period of 100 years equivalent to an inflow of 673 m3/day. Finally, the results indicate that, at least in the conditions of the city of Temuco, the use of permeable interlocking concrete pavement (PICP) proves to be a sustainable and feasible alternative to implementing measures of adaptation and mitigation against climate change, reducing the city’s flooding zones and allowing the irrigation of urban green areas.

Extreme Compound and Seesaw Hydrometeorological Events in New Zealand: An Initial Assessment

AbstractAttention is increasingly being turned toward land atmosphere interactions within the wider hydrological cycle when investigating extreme hydrometeorological events. This is particularly the case with the identification of compound and seesaw events. To do so, accurate soil moisture data are essential. Here, soil moisture from three reanalysis products (ERA5‐Land, BARRA and ERA5) is compared to station observations from 12 sites across New Zealand, for an average timespan of 18 years. Soil moisture data from all three reanalyses are used to investigate land‐atmosphere coupling with gridded (observational) precipitation and temperature. This enables compound (co‐occurrence of hot and dry) and seesaw (rapid transitions from dry to wet) events to be identified and examined. No best performing reanalysis data set for soil moisture is evident (median Pearson's r range: 0.78–0.81). All reanalyses successfully capture the seasonal and residual component of soil moisture, but not the observed soil moisture trends at each location. Strong coupling between soil moisture and temperature occurs in all three reanalyses across the predominately energy‐limited regions of the lower North Island and entire South Island. Consequently, these regions reveal a high frequency of compound event occurrence and potential shifts in land states to a water limited phase during compound months. A series of seesaw events is also detected for the first time in New Zealand (terminating approximately one‐fifth of drought events), with a high frequency of seesaw event occurrence detected in previously identified areas of atmospheric river (AR) activity.

West-WRF 34-Year Reforecast: Description and Validation

Abstract This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) Model, tailored for the prediction of extreme hydrometeorological events over the western United States (West-WRF) spanning 34 cool seasons (1 December–31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering western North America and the eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast relative to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin scale, the reforecast can improve MAP relative to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coast Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high-resolution (<25 km) regional models. The applications of this high-resolution multiyear reforecast include process-based studies, assessment of model performance, and machine learning applications.

Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions

Extreme hydrometeorological events such as the 2018 Vaia storm increasingly threaten alpine regions with multiple hazards often compounded and with cascading effects. Currently available risk assessment and prevention tools may therefore prove inadequate, particularly for transborder and vulnerable mountain areas, calling for comprehensive multi-hazard and transdisciplinary approaches. In particular, the exposed assets should not anymore be considered a sheer collection of static items, but the models should also reflect functional features. In this paper, we propose an integrated approach to multi-hazard exposure modelling including both static and functional components. The model is based on a homogeneous planar tessellation composed of hexagonal cells and a graph-like structure which describes the functional connections among the cells. To exemplify the methodology, a combination of static (buildings, protective forests), dynamic (population) and functional (road-based transport system) components has been considered together, targeting a ca. 10,000 km2 region across Italy and Austria. A cell-based aggregation at 250 m resolution and an innovative graph-based simplification allow for a good trade-off between the complexity of the model and its computational efficiency for risk-related applications. Furthermore, aggregation ensures protection of sensitive data at a scale still useful for civil protection. The resulting model can be used for different applications including scenario-based risk analysis and numeric simulation, probabilistic risk assessment, impact forecasting and early warning.

Tendencias espacio-temporales de precipitación y temperatura, así como cambios en clases climáticas de Köppen-Geiger en la subcuenca del río Sila, México (1956-2015)

According to various national and international institutions, which position Mexico as one of the most vulnerable countries to the effects of climate variability, such as extreme hydrometeorological events and climate change, with intensify some of these events. The research was conducted with the purpose of understanding how temperature and precipitation have behaved over a 60-year period (1956-2015), based on the analysis of monthly data from meteorological stations, using process automation (to structure geospatial data, perform interpolations, and classify climate according to Köppen-Geiger), and the use of geospatial tools, in one of the most important sub-basins as a source of drinking water supply, both for the region and for Mexico City, and one of the main agricultural areas in the State of Mexico. The results of the three analyses conducted in this study demonstrate that, although there have been some changes in precipitation and temperature values, these variations have not implied a change in the current climate type. However, it is not ruled out that this situation may be altered at the watershed level, as other authors assert, mentioning that precipitation is decreasing while temperatures are rising throughout the central region of the country. Therefore, integrated environmental conservation actions within the Sila river sub-basin are necessary to maintain current climatic conditions, thus safeguarding the well-being of future generations.

Assessing seasonal variation and trends in rainfall patterns of Madhya Pradesh, Central India

Abstract Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels.

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5°C, 2°C, and 3°C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3°C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

Innovative Methods for Mapping the Suitability of Nature-Based Solutions for Landslide Risk Reduction

The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of large-scale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide risk reduction.

Climate Risk Analysis Using a High-Resolution Spatial Model in Costa Rica

Increasing extreme weather and climate events have led to recurrent disasters that cause significant harm to human populations. The aim of this present study is to complement Costa Rica’s National Meteorological Institute 2017 risk assessment methodology for extreme weather and climate events. This methodology uses different socio-spatial indicators related to vulnerability and hazards to extreme hydrometeorological events. However, in the methodology applied and presented in this document, an exposure component was added to the model to obtain a more detailed representation of climate-related risks. The presented methodology was implemented in the municipalities of Cartago and Turrialba, where the frequency and severity of weather events have been a major issue in the last few decades. The results showed in fact, there were considerable differences between both, whereby factoring in the exposure component it is possible to visualize more specific risk zones, which evidences the need to include exposure components in this type of model, as this may allow for more timely responses and disaster risk prevention according to the specific vulnerability conditions in these zones. The outcomes at the minimal geostatistical unit level can aid local decision makers in developing more effective disaster risk management and adaptation strategies to minimize the loss of life and property resulting from extreme weather and climate events in Costa Rica.

Planned Adaptation of Traditional Dwellings to Extreme Hydrometeorological Events in Indigenous Peoples in the Bolivian Gran Chaco

Las afectaciones ocasionadas por eventos hidrometeorológicos extremos de origen natural como sequías e inundaciones en las viviendas tradicionales en la provincia del Gran Chaco en Bolivia son frecuentes. En ellas inciden las técnicas de construcción usadas, los materiales de construcción empleados, la localización y el diseño de las viviendas, porque no resisten a las amenazas de dichos eventos extremos. Estos aspectos comprometen el hábitat de las poblaciones que las ocupan. Es por ello que esta investigación propone desarrollar desde las bases teóricas de la adaptación planificada como componente de la resiliencia urbano-territorial, la mejora de los sistemas socio- ambientales ante los efectos de los eventos hidrometeorológicos extremos a través de dichas viviendas tradicionales. Es desde la investigación cualitativa que se apoya esta pesquisa para reconocer variables y atributos apoyados desde los enfoques interdisciplinar y sistémico. Los mismos que permiten conectar la relación entre las afectaciones climáticas y los pueblos originarios, buscando desde la práctica social la mejora de las viviendas mediante soluciones ingeniería amparadas en un grupo de regulaciones territoriales en el ámbito rural, así como en la construcción de políticas públicas desde el ente regulador correspondiente.

Climate Change and Cultural Heritage: Methods and Approaches for Damage and Risk Assessment Addressed to a Practical Application

In the last 20 years, research on the observed and projected impacts of climate change on cultural heritage has led to significant developments regarding damage quantification and risk assessment, which unfortunately are not yet exhaustively transferred to practical applications and to the sector of policy and decision making. One of the major reasons for this still lacking alignment remains with the inadequate handover of quantitative data, which is a prerequisite for the development of measures and strategies for the mitigation of the impacts and risk reduction. In this paper, we focus on the methods and approaches put in place for the production of projections providing quantitative assessments of climate change-induced impacts in the near and far future (up to the 21st century) on outdoor built heritage mainly constituted by stone and stone-like materials. Our critical study found that different approaches have been applied for quantifying slow cumulative damage due to the ongoing variations of climate and air pollution parameters and to risk assessment caused by hydrometeorological extreme events induced by variations of temperature and precipitation. There is clear evidence that efforts are still needed for directing research to provide concrete solutions and tools addressed to meet the requirements of stakeholders and to solve the existing challenges in the field: selected effective models and tools are illustrated. The discussion is structured in order to highlight the driving role of research in supporting the definition of priorities for heritage managers and the development of strategies by decision and policy makers for the prevention and safeguarding of cultural heritage at risk.

Mixture Probability Models with Covariates: Applications in Estimating Risk of Hydroclimatic Extremes

Modeling of extreme events is important in many scientific fields, including environmental and civil engineering, and impacts and risk assessments. Among available methods, statistical models that allow estimating extremes’ frequency and intensity are regularly used in procedures to anticipate potential changes in extreme events. Extreme value theory provides a theoretical basis for statistical estimation of extreme events using frequency analysis. The challenge in modeling is knowing when to use the block maxima method or the peaks-over-threshold (POT) method. Each has its drawbacks. POT describes the main characteristics of the observed extreme series; the threshold selection is always challenging and might affect the accuracy of the simulated results and the credibility of changes in extreme values. To encompass this challenge, mixture models offer more flexibility to represent samples with nonhomogeneous data. This study presents the gamma generalized Pareto (GGP) mixture model for estimating risk occurrence of hydroclimatic extremes. The model was developed in its general form, whereas the observed hydrometeorological extreme events depend on multidimensional covariates. A maximum likelihood algorithm is proposed to estimate the parameters with a constraint on the shape parameter of the generalized Pareto (GP) distribution. A Monte Carlo (MC) simulation compared the proposed model with the classical POT approach, with a fixed threshold, and the annual maximum series of streamflow. The approach was applied using a daily hydrological data set from an observed station located in the Saint John River at Fort Kent (01AD002), New Brunswick, Canada. The results show a flexibility to model extremes for dependent or nonstationary time series and adequately describes the central part of the observed frequencies, as well as the tails.