Preprints

Filtering by Subject: Applied Statistics

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Ratna Satyaningsih, Victor Jetten, Janneke Ettema, et al.

Published: 2023-01-20
Subjects: Applied Statistics, Earth Sciences, Geomorphology, Hydrology, Meteorology

High spatiotemporal resolution satellite data have been available to provide rainfall estimates with global coverage and relatively short latency. On the other hand, a rain gauge measures the actual rain that falls to the surface, but its network density is commonly sparse, particularly those that record at sub-daily records. These datasets are extensively used to define rainfall thresholds for [...]

From ground motion simulations to landslide occurrence prediction

Ashok Dahal, David Alejandro Casto Cruz, Hakan Tanyas, et al.

Published: 2023-01-17
Subjects: Applied Statistics, Earth Sciences, Geomorphology, Geophysics and Seismology, Physical Sciences and Mathematics, Soil Science, Statistical Models, Statistics and Probability

Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslide distribution. To address this gap, we developed [...]

Estimation and Uncertainty Quantification of Magma Interaction Times using Statistical Emulation

Luca Insolia, St├ęphane Guerrier, Chiara Paola Montagna, et al.

Published: 2022-12-02
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Volcanology

Evolution of volcanic plumbing systems towards eruptions of different styles and sizes largely depends on processes at crustal depths that are outside our observational capabilities. These processes can be modeled and the outputs of the simulations can be compared with the chemistry of the erupted products, geophysical and geodetic data to retrieve information on the architecture of the plumbing [...]

Standardised indices to monitor energy droughts

Sam Allen, Noelia Otero

Published: 2022-12-01
Subjects: Applied Statistics, Environmental Sciences, Physical Sciences and Mathematics, Sustainability

To mitigate the effects of climate change, energy systems are becoming increasingly reliant on renewable energy sources. Since these energy sources are typically dependent on the prevailing weather, renewable energy systems are susceptible to shortages during certain weather conditions. As renewable sources become larger contributors to the energy mix, the risks associated with these shortages, [...]

Fisher Discriminant Analysis for Extracting Interpretable Phenological Information from Multivariate Time Series Data

Conor Doherty, Meagan S Mauter

Published: 2022-10-24
Subjects: Applied Statistics, Environmental Engineering, Environmental Monitoring, Environmental Sciences, Remote Sensing

For many applications in environmental remote sensing, the interpretation of a given measurement depends strongly on what time of year the measurement was taken. This is particularly the case for phenology studies concerned with identifying when plant developmental transitions occur, but it is also true for a wide range of applications including vegetation species classification, crop yield [...]

Space-time landslide size modelling in Taiwan

Zhice Fang, Yi Wang, Cees J. van Westen, et al.

Published: 2022-10-10
Subjects: Applied Statistics, Geomorphology, Multivariate Analysis, Statistical Models

Landslide susceptibility assessment using data-driven models has predominantly focused on predicting where landslides may occur and not on how large they might be. The spatio-temporal evaluation of landslide susceptibility has only recently been addressed, as a basis for predicting where and when landslides might occur. The present study combines these new developments by proposing a data-driven [...]

Machine learning for understanding inland water quantity, quality, and ecology

Alison Paige Appling, Samantha Kay Oliver, Jordan S. Read, et al.

Published: 2022-09-03
Subjects: Applied Statistics, Fresh Water Studies, Hydrology

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; [...]

Space-time landslide susceptibility modelling in Taiwan

Zhice Fang, Yi Wang, Cees J. van Westen, et al.

Published: 2022-07-27
Subjects: Applied Statistics, Geomorphology, Multivariate Analysis, Statistical Models

Portraying spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we implement a space-time modeling approach to predict the landslide susceptibility on a yearly basis across the main island of Taiwan, from 2004 to 2018. We use a Bayesian version of a binomial generalized additive model, which assumes that landslide [...]

The Lock-Down Effects of COVID-19 on the Air Pollution Indices in Iran and Its Neighbors

Mohammad Fayaz

Published: 2022-07-02
Subjects: Applied Statistics, Environmental Public Health, Environmental Sciences, Environmental Studies, Near and Middle Eastern Studies, Other Statistics and Probability, Statistical Methodology, Statistical Models

Introduction The Covid-19 restrictions have a lot of various peripheral negative and positive effects like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world. Method Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air [...]

Space-time landslide hazard modeling via Ensemble Neural Networks

Ashok Dahal, Hakan Tanyas, Cees J. van Westen, et al.

Published: 2022-06-02
Subjects: Applied Statistics, Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics, Statistics and Probability

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the [...]

A univariate extreme value analysis and change point detection of monthly discharge in Kali Kupang, Central Java, Indonesia

Sandy Hardian Susanto Herho

Published: 2022-05-19
Subjects: Applied Statistics, Hydrology, Probability, Water Resource Management

This study presents how Extreme Value Analysis (EVA) can be used to predict future extreme hydrological events and how dynamic-programming based change point detection algorithm can be used to detect the abrupt transition in discharge events variability in Kali Kupang, Central Java, Indonesia. By using the annual block maxima, we can predict the upper extreme discharge probability from the Gumbel [...]

Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

Thorsten Hoeser, Claudia Kuenzer

Published: 2022-04-26
Subjects: Applied Statistics, Earth Sciences, Environmental Sciences, Natural Resource Economics, Natural Resources Management and Policy, Oil, Gas, and Energy, Other Earth Sciences, Statistics and Probability

With the promotion of renewable energy production and a planned phaseout of fossil fuels until 2040, the offshore wind energy sector has started to expand and will continue to increase its capacity in the upcoming decades. This study presents how the installed capacity can be derived from radar imagery provided by the Sentinel-1 mission for all offshore wind turbines on the entire Earth. By [...]

GEDI Launches a New Era of Biomass Inference from Space

Ralph Dubayah, John Armston, Sean Healey, et al.

Published: 2022-04-20
Subjects: Applied Statistics, Earth Sciences, Other Earth Sciences, Other Forestry and Forest Sciences, Terrestrial and Aquatic Ecology

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a [...]

Place-level urban-rural indices for the United States from 1930 to 2018

Johannes H. Uhl, Lori M. Hunter, Stefan Leyk, et al.

Published: 2022-02-28
Subjects: Applied Statistics, Geographic Information Sciences, Geography, Human Geography, Longitudinal Data Analysis and Time Series, Social and Behavioral Sciences, Spatial Science

Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant [...]

Uncertainty and sensitivity analysis for probabilistic weather and climate risk modelling: an implementation in CLIMADA v.3.1.

Chahan M. Kropf, Alessio Ciullo, Laura Otth, et al.

Published: 2022-02-23
Subjects: Applied Statistics, Climate, Design of Experiments and Sample Surveys, Earth Sciences, Environmental Studies, Natural Resources Management and Policy, Nature and Society Relations, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Risk Analysis, Statistical Methodology

Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here we present a new feature of the climate risk modelling platform CLIMADA which allows to carry out global uncertainty and sensitivity analysis. CLIMADA underpins [...]

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