• Energy Research

The livelihoods of people in the Andes are expected to be affected by climate change due to their dependence on glacier water. The observed decrease in glacier volume over the last few decades is likely to accelerate during the current century, which will affect water availability in the region. This paper presents an approach for participatory development of community-based adaptation measures to cope with the projected impacts of climate change. It combines in an innovative manner participatory design with physical measurements, modeling and a vulnerability analysis. Vulnerability to drought is made operational for households in a catchment of the Ocoña River basin in Peru. On the basis of a household survey (n = 94) we explore how a vulnerability index (risk divided by response efficacy) can be used to assess the distribution of vulnerability over households, and how socio-economic factors determine this vulnerability. Water entitlement, area of irrigated land, income and education are all significantly correlated with vulnerability to drought. The research showed that the main source of spring water is local rainwater, and that water use efficiency is low. The selected adaptation measures aimed to increase water availability close to farmland, and increase water use efficiency of farmers and households.

<p>Current global riverine flood risk studies assume a constant mean sea level boundary. In reality, high sea levels can propagate up a river leading to elevated water levels, and/or the drainage of high river discharge can be impeded by elevated sea levels. Riverine flood risk in deltas might therefore be underestimated if dynamic sea levels are ignored. This contribution presents the first global scale assessment of drivers of riverine flooding in deltas and underlines the importance of including dynamic downstream sea level boundaries in global riverine flood risk studies.</p><p>The assessment is based on extreme water levels at 3433 river mouth locations as modeled by the state-of-the-art global river routing model CaMa-Flood, forced with a multi-model runoff ensemble from the EartH2Observe project and bounded by dynamic sea level conditions from the global tide and surge model GTSM. Using this framework, we classified the drivers of riverine flooding at each location into four classes: surge dominant, discharge dominant, compound or insignificant. The classification is based on rank correlations between annual maximum riverine water levels and surge levels, and annual maximum riverine water levels and discharge. We developed a model experiment to quantify the effect of surge on flood levels and impacts.</p><p>We find that drivers of riverine flooding are compound at 19.7 % of the locations analyzed, discharge dominant at 69.2 % and surge dominant at 7.8 %. Compared to locations with either surge or discharge dominant flood drivers, locations with compound flood drivers generally have larger surge extremes, are located in basins with faster discharge response and/or flat topography. Globally, surge exacerbates 1-in-10 years flood levels at 64.0 % of the locations analyzed, with a mean increase of 13.5 cm. While this increase is the largest at locations with compound or surge dominant flood drivers, surge also affects flood levels at locations with discharge dominant flood drivers. A small decrease in 1-in-10 years flood levels is observed at 12.2 % of locations analyzed due to negative seasonal component of surge associated with dominant seasonal gyre circulations. Finally, we show that if surge is ignored, flood depths are underestimated for 38.2 million out of a total of 332.0 million (11.6 %) expected annual mean people exposed to riverine flooding.</p>

Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM climate change datasets (see https://figshare.com/s/397aff8631a7da2843fc) Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD datasets contain maximum wind speeds for a fixed set of return periods at 10 km resolution in every basin and for every climate model used here (see below). The STORM_FIXED_WIND_SPEED dataset contains return periods for a fixed set of maximum wind speeds at 10 km resolution in every ocean basin. The STORM_CITIES dataset contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from a selection of 18 coastal cities. The STORM_ISLANDS contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from the capital city of an island. We included the Small Island Developing States and a set of other islands.

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.

These are the Python scripts and files necessary to recreate the STORM future climate scripts presented in Bloemendaal et al (2022) "A globally consistent local-scale assessment of future tropical cyclone risk". Please read the README before using the scripts. We recommend STORM users to also read the following documentation: Bloemendaal et al (2020) "Generation of a global synthetic tropical cyclone hazard dataset using STORM" (https://www.nature.com/articles/s41597-020-0381-2); Bloemendaal et al (2020) "Estimation of global tropical cyclone wind speed probabilities using the STORM dataset" (https://www.nature.com/articles/s41597-020-00720-x) The entire STORM repository can (also) be found on Github, see www.github.com/NBloemendaal. This also includes updates to the scripts. {"references": ["Bloemendaal et al (2022) A globally consistent local-scale assessment of future tropical cyclone risk", "Bloemendaal et al (2020) Generation of a global synthetic tropical cyclone hazard dataset using STORM", "Bloemendaal et al (2020) Estimation of global tropical cyclone wind speed probabilities using the STORM dataset"]}

There is considerable uncertainty surrounding future changes in tropical cyclone (TC) frequency and intensity, particularly at local scales. This uncertainty complicates risk assessments and implementation of risk mitigation strategies. We present a novel approach to overcome this problem, using the statistical model STORM to generate 10,000 years of synthetic TCs under past (1980–2017) and future climate (SSP585; 2015–2050) conditions from an ensemble of four high-resolution climate models. We then derive high-resolution (10-km) wind speed return period maps up to 1000 years to assess local-scale changes in wind speed probabilities. Our results indicate that the probability of intense TCs, on average, more than doubles in all regions except for the Bay of Bengal and the Gulf of Mexico. Our unique and innovative methodology enables globally consistent comparison of TC risk in both time and space and can be easily adapted to accommodate alternative climate scenarios and time periods.