Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics
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Advancing operational flood forecasting, early warning and risk management with new emerging science: gaps, opportunities and barriers in Kenya
Get PDFKenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances
Shared environmental similarity between relatives influences heritability of reproductive timing in wild great tits
Get PDFIntraspecific variation is necessary for evolutionary change and population resilience, but the extent to which it contributes to either depends on the causes of this variation. Understanding the causes of individual variation in traits involved with reproductive timing is important in the face of environmental change, especially in systems where reproduction must coincide with seasonal resource availability. However, separating the genetic and environmental causes of variation is not straightforward, and there has been limited consideration of how small-scale environmental effects might lead to similarity between individuals that occupy similar environments, potentially biasing estimates of genetic heritability. In ecological systems, environments are often complex in spatial structure, and it may therefore be important to account for similarities in the environments experienced by individuals within a population beyond considering spatial distances alone. Here, we construct multi-matrix quantitative genetic animal models using over 11,000 breeding records (spanning 35 generations) of individually-marked great tits (Parus major) and information about breeding proximity and habitat characteristics to quantify the drivers of variability in two key seasonal reproductive timing traits. We show that the environment experienced by related individuals explains around a fifth of the variation seen in reproductive timing, and accounting for this leads to decreased estimates of heritability. Our results thus demonstrate that environmental sharing between relatives can strongly affect estimates of heritability and therefore alter our expectations of the evolutionary response to selection
An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland
Get PDFAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description
Machine learning for stochastic parametrization
Get PDFAtmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain
Bayesian views of generalized additive modelling
Get PDF•Generalized additive models (GAMs) are a frequently used, flexible framework applied to many problems in statistical ecology. They are commonly used to incorporate smooth effects into models via splines, including spatial components in species distribution models.
•GAMs are often considered to be a purely frequentist framework (‘generalized linear models with wiggly bits’), however links between frequentist and Bayesian approaches to these models were highlighted early‐on in the literature. From a practical perspective, Bayesian thinking underlies many parts of the implementation in the popular R package mgcv , so understanding these underpinnings can be informative during model building and assessment.
•This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, as detailed in the statistical literature, in accessible way, with a focus on the mgcv implementation. By harnessing these links we can expand the set of modelling tools we have at our disposal, as well as our understanding of how existing methods work.
•Two important topics for quantitative ecologists are covered in detail: model term selection and uncertainty estimation. Taking Bayesian viewpoints for these problems makes them much more tractable in many applied settings. Examples are given using data from the NOAA Alaska Fisheries Science Center's groundfish assessment program
UK hydrological outlook - March 2025
Get PDFThe Hydrological Outlook provides an insight into future hydrological conditions across the UK. Specifically, it describes likely trajectories for river flows and groundwater levels on a monthly basis, with a particular focus on the next three months.
Well established monitoring programmes provide the current status of both river flows and groundwater levels at many sites across the UK, and data from these programmes provide the starting point for the Outlook. A number of techniques are used to project forwards from the current state and results from these are used to produce a summary that includes a highlights map
Improving the reproducibility in geoscientific papers: lessons learned from a Hackathon in climate science
Get PDFIn this paper, we explore the crucial role and challenges of computational reproducibility in geosciences, drawing insights from the Climate Informatics Reproducibility Challenge (CICR) in 2023. The competition aimed at (1) identifying common hurdles to reproduce computational climate science; and (2) creating interactive reproducible publications for selected papers of the Environmental Data Science journal. Based on lessons learned from the challenge, we emphasize the significance of open research practices, mentorship, transparency guidelines, as well as the use of technologies such as executable research objects for the reproduction of geoscientific published research. We propose a supportive framework of tools and infrastructure for evaluating reproducibility in geoscientific publications, with a case study for the climate informatics community. While the recommendations focus on future CIRCs, we expect they would be beneficial for wider umbrella of reproducibility initiatives in geosciences
UK hydrological outlook - February 2025
Get PDFThe Hydrological Outlook provides an insight into future hydrological conditions across the UK. Specifically, it describes likely trajectories for river flows and groundwater levels on a monthly basis, with a particular focus on the next three months.
Well established monitoring programmes provide the current status of both river flows and groundwater levels at many sites across the UK, and data from these programmes provide the starting point for the Outlook. A number of techniques are used to project forwards from the current state and results from these are used to produce a summary that includes a highlights map
Global urbanization benefits food security and nature restoration
No full textUrbanization is often viewed as a threat to food security and environmental restoration due to extensive land use. However, by integrating urban and rural land perspectives, a different narrative emerges. Using data from 214 countries, we demonstrate that the projected urbanization of 2 billion people between 2020 and 2050 could unlock approximately 52 million hectares (Mha) of land, due to higher urban population densities. In scenarios with increased urban density, potential land savings could reach 80 Mha, meeting 55 % of the additional global cropland demand by 2050. If allocated for ecological restoration, this land could protect 1,437 species and sequester 21 billion tonnes (14–27 billion tonnes, 90 % confidence interval) of carbon by 2050. These findings underscore the positive impact that strategic urbanization can have on land use and conservation goals
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