Climate Change Studies Research Articles

Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations

Abstract. Error metrics are useful for evaluating model performance and have been used extensively in climate change studies. Despite the abundance of error metrics in the literature, most studies use only one or two metrics. Since each metric evaluates a specific aspect of the relationship between the reference data and model data, restricting the comparison to just one or two metrics limits the range of insights derived from the analysis. This study proposes a new framework and composite error metrics called Bergen metrics to summarize the overall performance of climate models and to ease interpretation of results from multiple error metrics. The framework of Bergen metrics are based on the p norm, and the first norm is selected to evaluate the climate models. The framework includes the application of a non-parametric clustering technique to multiple error metrics to reduce the number of error metrics with minimum information loss. An example of Bergen metrics is provided through its application to the large ensemble of regional climate simulations available from the EURO-CORDEX initiative. This study calculates 38 different error metrics to assess the performance of 89 regional climate simulations of precipitation and temperature over Europe. The non-parametric clustering technique is applied to these 38 metrics to reduce the number of metrics to be used in Bergen metrics for eight different sub-regions in Europe. These provide useful information about the performance of the error metrics in different regions. Results show it is possible to observe contradictory behaviour among error metrics when examining a single model. Therefore, the study also underscores the significance of employing multiple error metrics depending on the specific use case to achieve a thorough understanding of the model behaviour.

Selection of multiple ensemble representative CMIP5 climate models for climate change study in developing river basin: the case of Awash River Basin, Ethiopia

Selection of multiple ensemble representative CMIP5 climate models for climate change study in developing river basin: the case of Awash River Basin, Ethiopia

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Optical flow of temperature reveals climate change patterns for agriculture and forestry

The study of climate change's impact on terrestrial ecosystems is crucial for achieving societal and economic sustainability and human well-being. Domains like agriculture and forestry respond to climate change similarly to other terrestrial natural biomes, experiencing reductions in productivity, migration, and loss of biodiversity. However, there is a research gap in understanding climate velocity for different land cover and land use types, which could reveal vulnerability patterns in agriculture and forestry. To address this gap, we present the first study of climate velocity for major land cover and land use classes with a focus on major crop growing regions and functional forest types at the continental level. To enable this analysis, we introduce a new approach for estimating climate velocity based on optical flow. This approach allows us to conduct a global study, improve ecosystem interconnection representation, and reduce the model's sensitivity to noisy data and parametrization. Our study highlights the significant vulnerability to climate change of agriculture as a land cover type with a climate velocity 15% higher than of other vegetation land cover classes. Furthermore, the analysis of climate velocity for functional tree types reveals the highest expected impact on deciduous and evergreen needleleaf trees primarily concentrated in regions with the highest climate velocity, especially in sub-polar and temperate climates. Conversely, tropical forests dominated by evergreen broadleaf tree types exhibits marked variations in climate velocity with the highest rates in South America and Africa and three times lower in Asia. Additionally, our analysis of climate velocity for agriculture shows that North American croplands experience the highest climate velocity exceeding the global average by 32%. This trend is consistent for major crop types such as soybean that have higher climate velocity than globally for 49%, rapeseed and sunflower for 39%, cotton for 35%, maize for 32%, and wheat for 30%. Notably, areas dedicated to cereals, sugar beet, and sunflowers are located in zones with climate velocities higher than the global average by 15%, 12%, and 9%, respectively. These patterns indicate potential shifts in the agriculture and forestry structures in the near future that coherent with recent climate change studies and will have significant impact on the socio-economic dimension of humanity sustainability.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Research on Wind Speed Prediction Method Based on Feature Fusion

Wind speed occupies a central position in the study of atmospheric circulation and climate change, playing a crucial role in the accuracy and reliability of meteorological forecasting models. The current meteorological observation network, especially in remote and mountainous areas, faces challenges due to insufficient data resulting from low station density. This limitation hampers a comprehensive understanding of wind field dynamics, subsequently affecting the performance of forecasting models. To enhance the performance of meteorological prediction models, this study adopts an innovative approach that combines boxplot difference indexing and feature-level fusion techniques to filter key predictive features and reduce information redundancy. Furthermore, by integrating spatial location information, the contribution of geographic data to wind field prediction is enhanced, improving the quality of the fused data and thereby increasing the accuracy of model predictions. Experimental comparisons demonstrate that the integrated wind field dataset, post feature fusion, achieves better results across various deep learning models.

Women's experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas

Women's experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas

Assessment of climate change in different regions of Karnataka state

At the regional level, climate change has significant influences on crop productivity and food security. A climate change study was carried out using different parametric indices like rainfall attributes, temperature, and humidity from 58 years of climatic data (1964-2017) in Karnataka. The climatic period was divided into the Pre-climate change period- P1 (1964-1990) and the climate change period- P2 (1991-2017) with 27 years. The result shows annual rainfall and rainy days were increased in South Interior Karnataka (SIK) and Malnad regions and reduced in North Interior Karnataka (NIK) and Coastal regions. Dakshina Kannada, Yadgir, Kalabarugi, Udupi and Kodagu districts showed a significant reduction in receiving rainfall and an increase in Shivamogga, Hassan, Kolar and Chitradurga districts from the P1 to P2 period. NIK and SIK regions are highly prone to drought vulnerability compared to Malnad and Coastal regions. The occurrence of droughts wasincreasing,the temperature trend is increased and the relative humidity trend is decreasing in the P2 period. Assessment of climate variability in P1 and P2 helps to adopt preciseuse of water, nutrient and different crop-specific management in different zones of Karnataka.

Investigation of rainfall disaggregation with flexible timescales based on point process models

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator’s performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.

Evaluation of the climate models used by the IPCC based on the position of the ITCZ in the Equatorial South Atlantic

AbstractA number of the uncertainties in climate projections for the end of the century are due to the difficulty of general circulation models (GCMs) to represent large‐scale precipitating systems such as the Intertropical Convergence Zone (ITCZ) in the historical period. In this study, we evaluate the Coupled Model Intercomparison Project Phase 5 and 6 models based on the position of the ITCZ in the South Atlantic during the period in which it is climatologically further south (February–May). This period characterizes the rainy season of the Brazilian semiarid. The models were ranked according to two objective methods and validated through a qualitative analysis. The first method considered a success rate to evaluate the correct position of the areas with maximum precipitation and minimum pressure and meridional wind as representative of the mean position of the ITCZ. Among the 15 coupled GCMs analysed for each CMIP, those that best represented the position of the ITCZ were HADGEM.2‐AO, MIROC5 and CANESM2 from CMIP5 and CESM2 and MIROC6 from CMIP6. These models represented well the following characteristics of the ITCZ in February–May: maximum precipitation near the equator at the height of the coast of South America and with the wind convergence zone sloping north towards the African continent. The most common problem noticed in the other GCMs was to displace the ITCZ to the south of its observed mean position within the period February–May. Also, some GCMs presented a double ITCZ (GISS‐E2‐H, GDFL‐CM3 and IPSL‐CM5A‐LR from CMIP5 and INM‐CM5‐0 from CMIP6) following two high TSM cores. One GCM (ACCESS1‐3 from CMIP5) presented a relatively good performance, but the zone of greater precipitation was shifted to the east, being located in the centre of the Atlantic basin. Knowing how the GCMs represent the ITCZ is a criterion that must be considered when choosing the GCMs for climate change studies.

Dataset of stable isotopes of precipitation in the Eurasian continent

Abstract. Stable isotopes in precipitation can effectively reveal the process of atmospheric water circulation, serving as an effective tool for hydrological and water resource research, climate change, and ecosystem studies. The scarcity of stable isotope data in precipitation has hindered comprehension of the regional hydrology, climate, and ecology due to discontinuities on a temporal scale and unevenness on a spatial scale. To this end, we collated stable hydrogen and oxygen isotope data in precipitation from 842 stations in Eurasia from 1961 to 2022, totalling 51 752 data records. Stable isotopes in precipitation across various regions of Eurasia, as a whole, decrease with increasing latitude and distance from the coast. In the summer, stable isotopes in precipitation are relatively enriched, while in the winter, they are relatively depleted. In recent decades, the stable isotope values of Eurasian precipitation show an overall trend of increasing variation with the advancement of years, which is associated with global warming. Geographical location, underlying surface conditions, seasons, and atmospheric circulation are all factors that determine the characteristics of stable isotopes in precipitation. The dataset of stable isotopes in Eurasian precipitation provides a powerful tool for understanding changes in regional atmospheric water circulation and assists in conducting hydrological, meteorological, and ecological studies in related regions. The datasets are available at https://doi.org/10.17632/rbn35yrbd2.2 (Zhu, 2024).

A data-driven probabilistic network approach to assess model similarity in CMIP ensembles

Abstract The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple Global Climate Models (GCMs) from different modeling centres with some shared building blocks and inter-dependencies. Applications typically follow the ‘model democracy’ approach which might have significant implications in the resulting products (e.g. large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multi-model uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study we apply Probabilistic Network Models (PNMs), a well established machine learning technique, as a new a posteriori method to measure inter-model similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multi-model ensemble and different reanalysis gridded datasets. PNMs are capable to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods, but have further explanatory potential building on probabilistic model querying.

Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

AbstractThe seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles.

Airborne atmospheric carbon dioxide measurement using 1.5  µm laser double-pulse IPDA lidar over a desert area.

An integrated path differential absorption (IPDA) lidar can accurately measure regional C O 2 weighted column average concentrations (X C O 2), which are crucial for understanding the carbon cycle in climate change studies. To verify the performance and data inversion methods of space-borne IPDA lidar, in July 2021, we conducted an airborne lidar validation experiment in Dunhuang, Gansu Province, China. An aircraft was equipped with a lidar system developed to measure X C O 2 and an in situ greenhouse gas analyzer (GGA). To minimize measurement errors, energy monitoring was optimized. The system bias error of the DAOD was determined by changing the laser output mode from the off/on to the on/on mode. The X C O 2 inversion results obtained through comparing the schemes of averaging signals before "log (logarithm)" and averaging after "log" indicate that the former performs better. The IPDA lidar measured X C O 2 over the validation site at 405.57ppm, and both the IPDA lidar and GGA measured sudden changes in the C O 2 concentration. The assimilation data showed a similar trend according to the altitude to the data measured by the in situ instrument. A comparison of the mean X C O 2 derived from the GGA results and assimilation data with the IPDA lidar measurements showed biases of 0.80 and 1.12ppm, respectively.

THE ROLE OF GEOMATICS ENGINEERING IN CLIMATE CHANGE STUDIES

Abstract. Geomatics Engineers acquire, manage, analyse, model and present the geo-spatially referenced data quickly, accurately and reliably. They can classify the derived data with geographic information systems and produce many models according to needs. This study focuses on the role of the geomatics discipline in combatting climate change, the capability of measuring climate change parameters and the determination of the causes of the parameters change. Basic information about the latest developments in the Geomatics discipline for climate change studies is given. Geomatics Engineer's studies for understanding Earth’s system and its response to climate change and models created for the solution are explained. Geomatics Engineering provides a wide range of geospatial data and produces applications for understanding climate change. They take place in climate change analyses by measuring various climate parameters, and by processing the atmosphere, land, glaciers and waters in various spatiotemporal analyses. The obtained results have shown the significantly important role of the geomatics discipline in obtaining, monitoring and analysing the data used for climate change effects’ mitigation. In conclusion, Geomatics Engineering contributes to the decision-makers who have a duty in struggling with climate change and all stakeholders of the subject together with practitioners at all levels to create a road map for the studies to be carried out.

Stratigraphic paleobiology

Abstract Stratigraphic paleobiology uses a modern understanding of the construction of the stratigraphic record—from beds to depositional sequences to sedimentary basins—to interpret patterns and guide sampling strategies in the fossil record. Over the past 25 years, its principles have been established primarily through forward numerical modeling, originally in shallow-marine systems and more recently in nonmarine systems. Predictions of these models have been tested through outcrop-scale and basin-scale field studies, which have also revealed new insights. At multi-basin and global scales, understanding the joint development of the biotic and sedimentary records has come largely from macrostratigraphy, the analysis of gap-bound packages of sedimentary rock. Here, we present recent advances in six major areas of stratigraphic paleobiology, including critical tests in the Po Plain of Italy, mass extinctions and recoveries, contrasts of shallow-marine and nonmarine systems, the interrelationships of habitats and stratigraphic architecture, large-scale stratigraphic architecture, and the assembly of regional ecosystems. We highlight the potential for future research that applies stratigraphic paleobiological concepts to studies of climate change, geochemistry, phylogenetics, and the large-scale structure of the fossil record. We conclude with the need for more stratigraphic thinking in paleobiology.

Impact of groundwater representation on heat events in regional climate simulations over Europe

Abstract. The representation of groundwater is simplified in most regional climate models (RCMs), potentially leading to biases in the simulations. This study introduces a unique dataset from the regional Terrestrial Systems Modelling Platform (TSMP) driven by the Max Planck Institute Earth System Model at Low Resolution (MPI-ESM-LR) boundary conditions in the context of dynamical downscaling of global climate models (GCMs) for climate change studies. TSMP explicitly simulates full 3D soil and groundwater dynamics together with overland flow, including complete water and energy cycles from the bedrock to the top of the atmosphere. By comparing the statistics of heat events, i.e., a series of consecutive days with a near-surface temperature exceeding the 90th percentile of the reference period, from TSMP and those from GCM–RCM simulations with simplified groundwater dynamics from the COordinated Regional Climate Downscaling EXperiment (CORDEX) for the European domain, we aim to improve the understanding of how groundwater representation affects heat events in Europe. The analysis was carried out using RCM outputs for the summer seasons of 1976–2005 relative to the reference period of 1961–1990. While our results show that TSMP simulates heat events consistently with the CORDEX ensemble, there are some systematic differences that we attribute to the more realistic representation of groundwater in TSMP. Compared to the CORDEX ensemble, TSMP simulates fewer hot days (i.e., days with a near-surface temperature exceeding the 90th percentile of the reference period) and lower interannual variability and decadal change in the number of hot days on average over Europe. TSMP systematically simulates fewer heat waves (i.e., heat events lasting 6 d or more) compared to the CORDEX ensemble; moreover, they are shorter and less intense. The Iberian Peninsula is particularly sensitive with respect to groundwater. Therefore, incorporating an explicit 3D groundwater representation in RCMs may be a key in reducing biases in simulated duration, intensity, and frequency of heat waves in Europe. The results highlight the importance of hydrological processes for the long-term regional climate simulations and provide indications of possible potential implications for climate change projections.

Assessing the potential risks of climate change on the natural capital of six countries resulting from global warming of 1.5 to 4 °C above pre-industrial levels

We present the results from a new framework providing an assessment of how climate change risks to natural capital accrue with warming of 1.5–4 °C in six countries (China, Brazil, Egypt, Ethiopia, Ghana, and India). Unlike typical biodiversity and climate change studies, this assessment also considers landcover and population changes across a range of 17 ecosystem services. The potential impacts of climate change (alone) on natural capital at 1.5 °C is greatest in Brazil and least in Ghana. However, when population and landcover change are included, areas projected to be at high natural capital risk begin to accrue by 1.5 °C in all countries. By 2 °C, Ethiopia and Ghana show increasing areas at high risk, even though they are at low risk owing to climate alone. Thus, current impacts to biodiversity and ecosystem services and changes in potential demand coupled with warming exceed changes projected by climate alone. However, this also indicates that there is adaptation potential, especially with warming of < 2 °C, to reduce risk through restoring habitat. At lower levels of warming, targeted restoration of marginal agricultural habitats would increase the bank of natural capital for use by people and provide support for remaining agricultural lands. By 3 °C, the adaptation potential from restoration is substantially less: < 1% in Brazil, India and Egypt; 7–8% in China and Ethiopia; but still 26% in Ghana. This indicates that restoration as an adaptation option for biodiversity, and thus, natural capital, rapidly decreases with increasing temperatures. By 2100, factoring in population change (SSP2), current ecological footprint, and current landcover, even with only 1.5 °C warming, large parts of Brazil, eastern China, most of Egypt, much of Ethiopia, southwestern Ghana (except for protected areas), and most of India are at high to extreme natural capital risk with an adaptation deficit potentially equating to a soft adaptation limit.

Insights intended to improve adaptation planning and reduce vulnerability at the local scale

We live in a world of constant change, where multiple factors that generate vulnerability coincide, such as pandemics, climate change, and globalization, among other political and societal concerns. This demands the development of approaches capable of dealing with diverse sources of vulnerability and strategies that enable us to plan for and mitigate harm in the face of uncertainty. Our paper shows that the interpretation and conception that one gives to vulnerability in climate change can influence how decision-making solutions and adaptation measures are proposed and adopted. In this context, our approach integrates contextual vulnerability and decision-making planning tools to bolster the capacity to adapt at a local scale. We link our analysis to the evolution of vulnerability in climate change studies and some core articles and decisions on climate change adaptation and capacity building under the United Nations Framework Convention on Climate Change (UNFCCC) and the Conference of Parties throughout this study.

Questioning the use of ensembles versus individual climate model generated flows in future peak flood predictions: Plausibility and implications

AbstractAccurate estimation of design floods is necessary for developing effective flood‐management strategies. Climate change (CC) studies on floods generally consider alterations in mean runoff using ensembles compared to a base period. In this study, we examined the plausibility and implications of applying individual climate model‐generated flows versus their ensembles to estimate peak floods (magnitude and timing of occurrence), using Budhigandaki River Basin of Nepal as a case study. Annual maximum one‐day floods were derived for four future climate scenario projections (cold‐dry, cold‐wet, warm‐wet, and warm‐dry) from simulated daily flow series. Future floods of six return periods estimated for the individual climate scenarios were compared with their “Ensemble” (combiner for the ensemble series is the arithmetic mean of daily floods), “Average,” and ‘Baseline.” Results showed that magnitudes of the flood peaks are such that those estimated using “Ensemble” &lt; “Average” &lt; individual series. We conclude that ensemble series should not be used for flood estimation because of the averaging effect. Designers should consider at the least the “Average” instead of the “Ensemble” series while designing climate‐resilient flood structures. Furthermore, the occurrences of flood peaks are likely to be confined within the monsoon season for the “Ensemble” but spread out in the other months for the individual climate scenarios. This could have direct implications on the availability and mobilization of resources as well as the need for a year‐round operational early warning system for flood risk management.