Preprints

Filtering by Subject: Statistical Models

The Potential for Fuel Reduction to Offset Climate Warming Impacts on Wildfire Intensity in California

Patrick T Brown, Scott Strenfel, Richard B. Bagley, et al.

Published: 2024-02-21
Subjects: Climate, Earth Sciences, Environmental Sciences, Forest Management, Forest Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Other Statistics and Probability, Physical Sciences and Mathematics, Probability, Statistical Methodology, Statistical Models, Statistics and Probability

Increasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using [...]

Bayesian network modelling of phosphorus pollution in agricultural catchments with high-resolution data

Camilla Negri, Per-Erik Mellander, Nicholas Schurch, et al.

Published: 2024-01-11
Subjects: Agriculture, Biochemistry, Environmental Monitoring, Statistical Models

A Bayesian Belief Network was developed to simulate phosphorus (P) loss in an Irish agricultural catchment. Septic tanks and farmyards were included to represent all P sources and assess their effect on model performance. Bayesian priors were defined using daily discharge and turbidity, high-resolution soil P data, expert opinion, and literature. Calibration was done against seven years of daily [...]

Designing and describing climate change impact attribution studies: a guide to common approaches

Colin J Carlson, Dann Mitchell, Tamma Carleton, et al.

Published: 2024-01-06
Subjects: Climate, Earth Sciences, Ecology and Evolutionary Biology, Environmental Public Health, Environmental Studies, Human Geography, Physical and Environmental Geography, Physical Sciences and Mathematics, Probability, Public Health, Spatial Science, Statistical Methodology, Statistical Models, Statistics and Probability

Impact attribution is an emerging transdisciplinary sub-discipline of detection and attribution, focused on the social, economic, and ecological impacts of climate change. Here, we provide an overview of common end-to-end frameworks in impact attribution, focusing on examples relating to the human health impacts of climate change. We propose a typology of study designs based on whether [...]

Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system

Mahnoor Ahmed, Hakan Tanyas, Raphaël Huser, et al.

Published: 2023-08-03
Subjects: Geomorphology, Statistical Models

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both [...]

Demystifying the Dynamics of Global and Regional Sea Level Trends from 1993 to 2021

Ashraf Rateb, Bridget R. Scanlon

Published: 2023-07-07
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Statistical Models

As global sea levels rise, questions persist about the robustness of trends and their dynamics. Here, we offer a fresh perspective by examining the dynamics of global and regional mean sea-level trends using a probabilistic framework applied to the altimetric record. We show that the global mean sea-level (GMSL) rise accelerated from 2.5 mm/yr (1993-2000) to 4.2 mm/yr (2014-2021) with an average [...]

Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia

Sandy Hardian Susanto Herho, Dasapta Erwin Irawan, Faiz Rohman Fajary, et al.

Published: 2023-06-30
Subjects: Climate, Hydrology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability, Sustainability, Water Resource Management

Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for [...]

A new OSL dose model to account for post-depositional mixing of sediments

Luke Andrew Yates, Zach Aandahl, Barry W. Brook, et al.

Published: 2023-06-07
Subjects: Earth Sciences, Statistical Models, Statistics and Probability

In applications of optically stimulated luminescence (OSL) dating to unconsolidated sediments, the burial age of a sample of grains is estimated using statistical models of the distribution of the experimentally determined equivalent doses of the grains, together with estimates of the environmental dose rate. For grains that have been vertically mixed after deposition (e.g., due to bioturbation), [...]

Carbon Utilization and Storage through Rehabilitation of Groundwater Wells

Vivek Vidyadhar Patil, Gabriella Basso, Steven Catania, et al.

Published: 2023-05-21
Subjects: Applied Statistics, Civil and Environmental Engineering, Climate, Earth Sciences, Environmental Sciences, Geochemistry, Hydrology, Longitudinal Data Analysis and Time Series, Oil, Gas, and Energy, Statistical Methodology, Statistical Models, Statistics and Probability

According to the Intergovernmental Panel on Climate Change (IPCC) of the United Nations (UN), rise in atmospheric concentration of carbon dioxide (CO2) due to anthropogenic factors is considered as the primary driver for global climate change. With almost every major corporation around the world working towards their “net-zero goals”, it is becoming increasingly important to have more [...]

Assessing multi-hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example

Letizia Elia, Silvia Castellaro, Ashok Dahal, et al.

Published: 2023-04-28
Subjects: Applied Statistics, Geomorphology, Glaciology, Statistical Models

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions, primarily because of the limited presence of human settlements and, therefore, the little need for risk assessment. However, global warming is radically changing this situation and [...]

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 [...]

Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

Mahmoud Al Najar, Rafael Almar, Erwin W. J. Bergsma, et al.

Published: 2023-01-10
Subjects: Artificial Intelligence and Robotics, Climate, Environmental Engineering, Environmental Monitoring, Geomorphology, Hydrology, Numerical Analysis and Scientific Computing, Oceanography, Sedimentology, Statistical Models

Given the current context of climate change and increasing population densities at coastal zones, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. This work makes use of Symbolic Regression, a type of Machine Learning algorithm, to evolve interpretable shoreline [...]

Efficient Estimation of Climate State and Its Uncertainty Using Kalman Filtering with Application to Policy Thresholds and Volcanism

John Matthew Nicklas, Baylor Fox-Kemper, Charles E Lawrence

Published: 2022-10-18
Subjects: Longitudinal Data Analysis and Time Series, Non-linear Dynamics, Planetary Sciences, Statistical Models

We present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model of the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA). It combines an annual energy balance model (difference equations) with 17 parameters drawn from the literature and a statistical Extended Kalman Filter assimilating GMST and OHCA, either observed timeseries or simulated by earth [...]

A data-driven framework for landslide size space-time modelling

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 [...]

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 [...]

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Rodrigo Miranda, Rodolfo L. B. Nobrega, Estevão Silva, et al.

Published: 2022-07-22
Subjects: Environmental Monitoring, Soil Science, Statistical Models

Land surface and Earth System models require reliable soil maps to represent the influence of spatial variability of soil properties on ecosystem fluxes and storages. However, mapping soils using conventional in situ survey protocols is time-consuming and costly. We addressed the outdated spatial information on soil physico-chemical properties for a tropical region with a ~700-km longitudinal [...]

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